CN117751688A

CN117751688A - Method and apparatus for wireless communication

Info

Publication number: CN117751688A
Application number: CN202180101262.5A
Authority: CN
Inventors: 陈景然
Original assignee: Guangdong Oppo Mobile Telecommunications Corp Ltd
Current assignee: Guangdong Oppo Mobile Telecommunications Corp Ltd
Priority date: 2021-11-01
Filing date: 2021-11-01
Publication date: 2024-03-22
Also published as: WO2023070684A1

Abstract

A method and apparatus for wireless communication, the method comprising: and the first terminal receives first information sent by core network equipment, wherein the first information is used for determining an artificial intelligence AI learning task group to which the first terminal belongs. Therefore, the terminal equipment can select the terminal equipment of the same AI learning task group as a relay terminal to establish connection with the network according to the first information, so that the transmission of training data or training results is realized, and the performance of the model is improved.

Description

Method and apparatus for wireless communication

Technical Field

The embodiment of the application relates to the field of communication, in particular to a method and equipment for wireless communication.

Background

Federal Learning (FL) is a machine Learning architecture, and the terminal device may train the global model according to small sample training data (or a subset of the training data) to obtain local training results, for example, the terminal device may input the small sample training data into the global model. The federal learning server can complete the training of the global model by aggregating the local training results reported by the plurality of terminals.

In some scenarios, the terminal participating in federal learning cannot complete all local training due to the problems of calculation power or electric quantity, or the terminal participating in federal learning moves out of coverage of a base station or a federal learning server, so that training data cannot be transmitted to the federal learning server, and how to train the federal learning model is a problem to be solved in this case.

Disclosure of Invention

The application provides a wireless communication method and device, wherein core network equipment can allocate an identification information for the same AI learning task group, and further, terminal equipment can select the terminal equipment of the same AI learning task group as a relay terminal according to the identification information to establish connection with a network, so that transmission of training data or training results is realized, and the performance of a model is improved.

In a first aspect, a method of wireless communication is provided, comprising: and the first terminal receives first information sent by core network equipment, wherein the first information is used for determining an artificial intelligence AI learning task group to which the first terminal belongs.

In a second aspect, there is provided a method of wireless communication, comprising: and the second terminal receives second information sent by the core network equipment, wherein the second information is used for determining an AI learning task group to which the second terminal belongs.

In a third aspect, a method of wireless communication is provided, comprising: the first core network equipment acquires identification information of terminal equipment in a first artificial intelligent AI learning task group; the first core network device distributes first AI learning identification information for the first AI learning task group, wherein the first AI learning identification information is used for identifying the first AI learning task group; and the first core network equipment sends the first AI learning identification information to the terminal equipment in the first AI learning task group.

In a fourth aspect, a method of wireless communication is provided, comprising: the second core network device sends a first relay service code to the first terminal, wherein the first relay service code corresponds to first artificial intelligence AI learning identification information, and the first AI learning identification information is used for identifying an AI learning task group to which the first terminal belongs.

In a fifth aspect, a terminal device is provided for performing the method of the first aspect or each implementation manner thereof.

Specifically, the terminal device comprises functional modules for performing the method of the first aspect or its implementation manner.

In a sixth aspect, a terminal device is provided for performing the method of the second aspect or each implementation manner thereof.

Specifically, the terminal device comprises functional modules for performing the method of the second aspect or implementations thereof.

A seventh aspect provides a core network device for performing the method of the third aspect or implementations thereof.

Specifically, the core network device comprises functional modules for performing the method of the third aspect or implementations thereof.

In an eighth aspect, a core network device is provided for performing the method in the third aspect or implementations thereof.

Specifically, the core network device comprises functional modules for performing the method of the fourth aspect or implementations thereof.

In a ninth aspect, a terminal device is provided, comprising a processor and a memory. The memory is used for storing a computer program, and the processor is used for calling and running the computer program stored in the memory and executing the method in the first aspect or various implementation manners thereof.

In a tenth aspect, a terminal device is provided, comprising a processor and a memory. The memory is for storing a computer program and the processor is for calling and running the computer program stored in the memory for performing the method of the second aspect or implementations thereof described above.

In an eleventh aspect, a core network device is provided that includes a processor and a memory. The memory is for storing a computer program and the processor is for calling and running the computer program stored in the memory for performing the method of the third aspect or implementations thereof.

In a twelfth aspect, a core network device is provided that includes a processor and a memory. The memory is for storing a computer program and the processor is for calling and running the computer program stored in the memory for performing the method of the fourth aspect or implementations thereof.

A thirteenth aspect provides a chip for implementing the method of any one of the first to fourth aspects or each implementation thereof.

Specifically, the chip includes: a processor for calling and running a computer program from a memory, causing a device in which the apparatus is installed to perform the method as in any one of the first to fourth aspects or implementations thereof described above.

In a fourteenth aspect, there is provided a computer-readable storage medium storing a computer program for causing a computer to perform the method of any one of the above first to fourth aspects or implementations thereof.

In a fifteenth aspect, there is provided a computer program product comprising computer program instructions for causing a computer to perform the method of any one of the first to fourth aspects or implementations thereof.

A fifteenth aspect provides a computer program which, when run on a computer, causes the computer to perform the method of any one of the above first to fourth aspects or implementations thereof.

Through the technical scheme, the core network device can allocate corresponding identification information for the same AI learning task group, and further can send indication information for determining the AI learning task group to which the terminal device belongs to the terminal device, for example, for a first terminal, the core network device can send first information to the first terminal, the first information is used for determining the AI learning task group to which the first terminal belongs, and for a second terminal, the core network device can send second information to the second terminal, the second information is used for determining the AI learning task group to which the second terminal belongs. The terminal device thus selects the relay terminal according to the indication information configured thereon. Further, training data or training results can be sent to the AI server through the selected relay terminal, so that the performance of the model can be improved.

Drawings

Fig. 1 is a schematic diagram of a communication system architecture provided in an embodiment of the present application.

Fig. 2 is a schematic diagram of a federal learning architecture provided in an embodiment of the present application.

Fig. 3 is a schematic diagram of a proximity service architecture provided in an embodiment of the present application.

Fig. 4 is a schematic interaction diagram of a method of wireless communication provided in accordance with an embodiment of the present application.

Fig. 5 is a schematic interaction diagram of a method of wireless communication according to one embodiment of the present application.

Fig. 6 is a schematic interaction diagram of a method of wireless communication according to another embodiment of the present application.

Fig. 7 is a schematic interaction diagram of a method of wireless communication according to yet another embodiment of the present application.

Fig. 8 is a schematic interaction diagram of a method of wireless communication according to yet another embodiment of the present application.

Fig. 9 is a schematic block diagram of a terminal device according to an embodiment of the present application.

Fig. 10 is a schematic block diagram of another terminal device provided according to an embodiment of the present application.

Fig. 11 is a schematic block diagram of a core network device according to an embodiment of the present application.

Fig. 12 is a schematic block diagram of a core network device according to an embodiment of the present application.

Fig. 13 is a schematic block diagram of a communication device provided according to an embodiment of the present application.

Fig. 14 is a schematic block diagram of a chip provided according to an embodiment of the present application.

Fig. 15 is a schematic block diagram of a communication system provided according to an embodiment of the present application.

Detailed Description

The following description of the technical solutions in the embodiments of the present application will be made with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden for the embodiments herein, are intended to be within the scope of the present application.

The technical solution of the embodiment of the application can be applied to various communication systems, for example: global system for mobile communications (Global System of Mobile communication, GSM), code division multiple access (Code Division Multiple Access, CDMA) system, wideband code division multiple access (Wideband Code Division Multiple Access, WCDMA) system, general packet Radio service (General Packet Radio Service, GPRS), long term evolution (Long Term Evolution, LTE) system, advanced long term evolution (Advanced long term evolution, LTE-a) system, new Radio (NR) system, evolved system of NR system, LTE-based access to unlicensed spectrum, LTE-U) system on unlicensed spectrum, NR (NR-based access to unlicensed spectrum, NR-U) system on unlicensed spectrum, non-terrestrial communication network (Non-Terrestrial Networks, NTN) system, universal mobile communication system (Universal Mobile Telecommunication System, UMTS), wireless local area network (Wireless Local Area Networks, WLAN), wireless fidelity (Wireless Fidelity, wiFi), fifth Generation communication (5 th-Generation, 5G) system, or other communication system, etc.

Generally, the number of connections supported by the conventional communication system is limited and easy to implement, however, with the development of communication technology, the mobile communication system will support not only conventional communication but also, for example, device-to-Device (D2D) communication, machine-to-machine (Machine to Machine, M2M) communication, machine type communication (Machine Type Communication, MTC), inter-vehicle (Vehicle to Vehicle, V2V) communication, or internet of vehicles (Vehicle to everything, V2X) communication, etc., and the embodiments of the present application may also be applied to these communication systems.

Optionally, the communication system in the embodiment of the present application may be applied to a carrier aggregation (Carrier Aggregation, CA) scenario, a dual connectivity (Dual Connectivity, DC) scenario, and a Stand Alone (SA) fabric scenario.

Optionally, the communication system in the embodiments of the present application may be applied to unlicensed spectrum, where unlicensed spectrum may also be considered as shared spectrum; alternatively, the communication system in the embodiments of the present application may also be applied to licensed spectrum, where licensed spectrum may also be considered as non-shared spectrum.

Fig. 1 is a schematic architecture diagram of a communication system according to an embodiment of the present application. As shown in fig. 1, the communication system includes: an access and mobility management function (Access and mobility management function, AMF) 101, a session management function (Session Management Function, SMF) 102, a radio access Network (Radio Access Network, RAN) 103, an authentication server function (Authentication Server Function, AUSF) 104, a unified Data management (Unified Data Management, UDM) 105, a policy control function (Policy Control function, PCF) 106, a Data Network (DN) 107, a User plane function (User Plane Function, UPF) 108, a User Equipment (UE) 109.

Wherein, UE 109 is connected with AMF 101 through N1 interface, UE 109 is connected with RAN 103 through radio resource control (Radio Resource Control, RRC) protocol; RAN 103 is connected with AMF 101 through N2 interface, RAN 103 is connected with UPF 108 through N3 interface; the UPFs 108 are connected through an N9 interface, the UPFs 108 are connected with the DN 107 through an N6 interface, and meanwhile, the UPFs 108 are connected with the SMF 102 through an N4 interface; SMF 102 is connected with PCF 106 through N7 interface, SMF 102 is connected with UDM 105 through N10 interface, at the same time, SMF 102 is connected with AMF 101 through N11 interface; the AMFs 101 are connected through an N14 interface, the AMFs 101 are connected with the UDM 105 through an N8 interface, the AMFs 101 are connected with the AUSF 104 through an N12 interface, and meanwhile, the AMFs 101 are connected with the PCF 106 through an N15 interface; the AUSF 104 is connected to the UDM 105 via an N13 interface. AMF 101 and SMF 102 obtain user subscription data from UDM 105 via the N8 and N10 interfaces, and policy data from PCF 106 via the N15 and N7 interfaces, respectively. The SMF 102 controls the UPF 108 over the N4 interface.

RAN 103 (or access network device) is an access device that UE 109 accesses to the network architecture in a wireless manner, and is mainly responsible for radio resource management, quality of service (quality of service, qoS) management, data compression, encryption, and the like on the air interface side.

In some embodiments of the present application, the Access network device may be a device for communicating with a mobile device, where the Access network device may be an Access Point (AP) in WLAN, a base station (Base Transceiver Station, BTS) in GSM or CDMA, a base station (NodeB, NB) in WCDMA, an evolved base station (Evolutional Node B, eNB or eNodeB) in LTE, a relay station or an Access Point, or a vehicle device, a wearable device, and an Access network device (gNB) in an NR network, or an Access network device in a PLMN network for future evolution, or an Access network device in an NTN network, etc.

By way of example and not limitation, in some embodiments of the present application, an access network device may have mobility characteristics such as: the access network device is a mobile device. Alternatively, the access network device may be a satellite, a balloon station. For example, the satellite may be a Low Earth Orbit (LEO) satellite, a medium earth orbit (medium earth orbit, MEO) satellite, a geosynchronous orbit (geostationary earth orbit, GEO) satellite, a high elliptical orbit (High Elliptical Orbit, HEO) satellite, or the like. Alternatively, the access network device may be a base station located on land, in water, etc.

In this embodiment of the present application, an access network device may provide a service for a cell, where a terminal device communicates with the access network device through a transmission resource (e.g., a frequency domain resource, or a spectrum resource) used by the cell, where the cell may be a cell corresponding to the access network device (e.g., a base station), and the cell may belong to a macro base station, or may belong to a base station corresponding to a Small cell (Small cell), where the Small cell may include: urban cells (Metro cells), micro cells (Micro cells), pico cells (Pico cells), femto cells (Femto cells) and the like, and the small cells have the characteristics of small coverage area and low transmitting power and are suitable for providing high-rate data transmission services.

In some embodiments of the present application, AMF 101, SMF102, AUSF 104, UDM 105, PCF 106, DN 107, and UPF 108 are network elements of a core network (abbreviated as core network element).

The AMF network element may be configured to manage access of the terminal to the core network, for example: location update of the terminal, registration of the network, access control, mobility management of the terminal, attachment and detachment of the terminal, and the like. The AMF network element may also provide a storage resource of the control plane for the session of the terminal in case of providing services for the session, to store a session identity, an SMF network element identity associated with the session identity, etc.

The SMF network element may be used to select a user plane network element for a terminal, redirect a user plane network element for a terminal, assign an internet protocol (internet protocol, IP) address for a terminal, establish a bearer (which may also be referred to as a session) between a terminal and a UPF network element, modify, release, and QoS control of a session.

The AUSF is configured to receive a request for authenticating the terminal by the AMF, and forward the issued key to the AMF for authentication by requesting the key to the UDM.

The UDM includes functions of generation and storage of user subscription data, management of authentication data, and the like, supporting interaction with an external third party server. The PCF network element is configured to provide policies, such as QoS policies, slice selection policies, etc., to the AMF network element, the SMF network element.

The DN may provide data services for users such as an IP Multimedia Service (IMS) network, the internet, etc. There may be various application servers (application server, AS) in the DN, providing different application services, such AS operator services, internet access or third party services, etc., and the AS may implement the AF function.

The UPF network element is mainly responsible for the transmission of user data, other network elements can be called control plane function network elements and are mainly responsible for authentication, registration management, session management, mobility management, policy control and the like so as to ensure the reliable and stable transmission of the user data.

The UPF network element may be used to forward and receive data of the terminal. For example, the UPF network element may receive data of a service from a data network, and transmit the data to a terminal through an access network device; the UPF network element may also receive user data from the terminal via the access network device and forward the user data to the data network. Wherein, the transmission resources allocated and scheduled by the UPF network element for the terminal are managed and controlled by the SMF network element. The bearer between the terminal and the UPF network element may include: user plane connection between a UPF network element and an access network device, and establishment of a channel between the access network device and a terminal. The user plane connection is a quality of service (quality of service, qoS) flow (flow) that can establish transmission data between the UPF network element and the access network device.

The AF network element is used for supporting the routing of application influence data in interaction with the core network element, accessing the network exposure function, interacting with the PCF network element to perform policy control and the like.

In some embodiments of the present application, a User Equipment (UE) may also be referred to as a terminal device, an access terminal, a subscriber unit, a subscriber station, a mobile station, a remote terminal, a mobile device, a User terminal, a wireless communication device, a User agent, or a User Equipment, etc.

The terminal device may be a STATION (ST) in a WLAN, may be a cellular telephone, a cordless telephone, a session initiation protocol (Session Initiation Protocol, SIP) phone, a wireless local loop (Wireless Local Loop, WLL) STATION, a personal digital assistant (Personal Digital Assistant, PDA) device, a handheld device with wireless communication capabilities, a computing device or other processing device connected to a wireless modem, a vehicle mounted device, a wearable device, a terminal device in a next generation communication system such as an NR network, or a terminal device in a future evolved public land mobile network (Public Land Mobile Network, PLMN) network, etc.

In embodiments of the present application, the terminal device may be deployed on land, including indoor or outdoor, hand-held, wearable or vehicle-mounted; can also be deployed on the water surface (such as ships, etc.); but may also be deployed in the air (e.g., on aircraft, balloon, satellite, etc.).

In the embodiment of the present application, the terminal device may be a Mobile Phone (Mobile Phone), a tablet computer (Pad), a computer with a wireless transceiving function, a Virtual Reality (VR) terminal device, an augmented Reality (Augmented Reality, AR) terminal device, a wireless terminal device in industrial control (industrial control), a wireless terminal device in unmanned driving (self driving), a wireless terminal device in remote medical (remote medical), a wireless terminal device in smart grid (smart grid), a wireless terminal device in transportation security (transportation safety), a wireless terminal device in smart city (smart city), or a wireless terminal device in smart home (smart home), and the like.

By way of example, and not limitation, in embodiments of the present application, the terminal device may also be a wearable device. The wearable device can also be called as a wearable intelligent device, and is a generic name for intelligently designing daily wear by applying wearable technology and developing wearable devices, such as glasses, gloves, watches, clothes, shoes and the like. The wearable device is a portable device that is worn directly on the body or integrated into the clothing or accessories of the user. The wearable device is not only a hardware device, but also can realize a powerful function through software support, data interaction and cloud interaction. The generalized wearable intelligent device includes full functionality, large size, and may not rely on the smart phone to implement complete or partial functionality, such as: smart watches or smart glasses, etc., and focus on only certain types of application functions, and need to be used in combination with other devices, such as smart phones, for example, various smart bracelets, smart jewelry, etc. for physical sign monitoring.

It should be noted that, the network architecture of the communication system shown in fig. 1 is not limited to the network architecture of the communication system in the embodiment of the present application, and in a specific implementation, the communication system may further include more or fewer network elements than shown in fig. 1, or some network elements may be combined. It should be understood that the RAN in fig. 1 may also be characterized as AN.

It should be understood that a device having a communication function in a network/system in an embodiment of the present application may be referred to as a communication device. Taking the communication system shown in fig. 1 as an example, the communication device may include an access network device (e.g., RAN 103), a core network element (e.g., PCF 106), and UE 109, which may have communication functions.

It should be understood that the terms "system" and "network" are used interchangeably herein. The term "and/or" is herein merely an association relationship describing an associated object, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.

It should be understood that, in the embodiments of the present application, the "indication" may be a direct indication, an indirect indication, or an indication having an association relationship. For example, a indicates B, which may mean that a indicates B directly, e.g., B may be obtained by a; it may also indicate that a indicates B indirectly, e.g. a indicates C, B may be obtained by C; it may also be indicated that there is an association between a and B.

In the description of the embodiments of the present application, the term "corresponding" may indicate that there is a direct correspondence or an indirect correspondence between the two, or may indicate that there is an association between the two, or may indicate a relationship between the two and the indicated, configured, or the like.

In the embodiment of the present application, the "predefining" may be implemented by pre-storing corresponding codes, tables or other manners that may be used to indicate relevant information in devices (including, for example, terminal devices and network devices), and the specific implementation of the present application is not limited. Such as predefined may refer to what is defined in the protocol.

It should be understood that the network device in the embodiments of the present application may include an access network device and a core network device.

In this embodiment of the present application, the "protocol" may refer to a standard protocol in the communication field, for example, may include an LTE protocol, an NR protocol, and related protocols applied in a future communication system, which is not limited in this application.

In order to facilitate understanding of the technical solutions of the embodiments of the present application, federal learning related to the present application is described.

With the increasing performance of cameras and sensors on mobile terminals, more and more terminals can collect valuable training data necessary for artificial intelligence (Artifact Intelligence, AI) model or Machine Learning (ML) model training. For many AI tasks or ML tasks, small sample training data collected by the mobile terminal is of great significance to training the global model.

Federal Learning (FL) is a machine Learning architecture, and the terminal device may train the global model according to small sample training data (or a subset of training data) to obtain local training results, for example, the terminal device may input the small sample training data into the global model (e.g. deep neural network (Deep Neural Networks, DNN)) to obtain intermediate training results (or local training results), such as gradient information of DNN. The FL server can complete the training of the global model by aggregating the local training results reported by a plurality of terminal devices.

FIG. 2 is a block diagram of federal learning architecture. In each iterative training, the terminal device may use the local training data to perform training on the global model downloaded from the federal learning server, and then report the intermediate training results (e.g., gradient information of DNN) to the federal learning server through an uplink channel. The federal learning server then aggregates the collected gradient information and updates the global model. The federal learning server distributes the updated global model to terminal equipment participating in federal learning through a downlink channel, and the terminal equipment carries out next iterative training aiming at the updated model.

In order to facilitate understanding of the technical solution of the embodiments of the present application, proximity-based Services (ProSe) related to the present application will be described.

A ProSe-capable terminal device may communicate directly with another ProSe-capable terminal device via a PC5 interface.

When a terminal device may be connected to an external data network through a 5G network and also has ProSe capability, the terminal device may act as a Relay terminal (Relay UE), another remote terminal (remote UE) having ProSe capability may establish a direct connection with the Relay terminal through a PC5 interface, and interact with the external network through a protocol data unit (Protocol Data Unit, PDU) session established by the Relay terminal with the 5G network, as shown in fig. 3.

In order to facilitate understanding of the technical solution of the embodiments of the present application, a device discovery process related to the present application is described.

The Remote UE and the Relay UE need to perform a discovery procedure before establishing a connection. The discovery process may employ either mode A (Model A) or mode B (Model B).

For mode a, for example, UE1 may send a discovery announcement message (Announcement message), or discovery notification message, as a Relay UE, notifying the Remote UE. The UE receiving the discovery announcement message may establish a connection with the UE1 when relay service is required, and transmit relay data through the UE 1.

For mode B, for example, UE1 may act as a Remote UE, and in order to find a Relay UE, UE1 may send a discovery request message (Solicitation message), or discovery summoning message, informing the Relay UE. The UE receiving the Solicitation message may reply to the UE1 with a discovery Response message (Response message) when the relay service can be performed, informing the UE1 that it can perform the relay service.

In some scenarios, the terminal participating in federal learning cannot complete all local training due to the problems of calculation power or electric quantity, or the terminal participating in federal learning moves out of coverage of a base station or a federal learning server, so that training data or training results cannot be transmitted to the federal learning server, and how to train a federal learning task model is a problem to be solved urgently.

In order to facilitate understanding of the technical solutions of the embodiments of the present application, the technical solutions of the present application are described in detail below through specific embodiments. The above related technologies may be optionally combined with the technical solutions of the embodiments of the present application, which all belong to the protection scope of the embodiments of the present application. Embodiments of the present application include at least some of the following.

Fig. 4 is a schematic interaction diagram of a method 200 of wireless communication according to an embodiment of the present application, as shown in fig. 4, the method 200 including at least part of the following:

s210, core network equipment sends first information to a first terminal, wherein the first information is used for determining an AI learning task group to which the first terminal belongs;

and S220, the core network equipment sends second information to the second terminal, wherein the second information is used for determining an AI learning task group to which the second terminal belongs.

In some embodiments of the present application, the core network device may be a core network element in the communication system shown in fig. 1.

In some embodiments of the present application, the first terminal may be a UE in the communication system shown in fig. 1, and the first terminal may be a specific device described in fig. 1, which is not described herein again.

In some embodiments of the present application, the second terminal may be a UE in the communication system shown in fig. 1, and the first terminal may be a specific device described in fig. 1, which is not described herein again.

It should be understood that the AI learning task group in the embodiment of the present application may be replaced by a terminal group, where the terminal group may be divided based on a task performed by a terminal device (for example, an AI learning task), or may be divided according to a capability of the terminal device, or may be divided according to a location of the terminal device, and the application is not limited to a specific grouping manner.

Alternatively, the terminal devices in the same terminal group may act as relay terminals for another terminal device. Hereinafter, the terminal group will be described as an example of the AI learning task group, but the present application is not limited thereto.

In the embodiment of the present application, the AI learning task group is also called AI task group, AI group, or ML learning task group, ML group, or the like, and the expressions described above may be replaced with each other.

In some embodiments, the AI learning task group may include, but is not limited to, a federal learning task group, a cluster learning task group, and the like.

In an embodiment of the application, the AI-learning task group includes a plurality of terminal devices for performing the same AI-learning task, such as a federal learning task.

In the embodiment of the application, the terminal devices in the same AI learning task group have the same AI model, for example, a DNN model.

In the embodiment of the present application, each AI learning task may correspond to a corresponding server, which may be referred to as an AI server, an AI learning task server, or the like.

Optionally, the AI server may be a federal learning server, and the federal learning server may aggregate training results of the terminal devices in the federal learning task group to complete training of the global model.

In some embodiments, the first terminal supports performing AI learning tasks, or the first terminal supports performing ML learning tasks, and the first terminal supports AI operations.

In some embodiments, the second terminal supports performing AI learning tasks, or the second terminal supports performing ML learning tasks, and the second terminal supports AI operations.

In some embodiments, the AI learning tasks supported by the first terminal and the second terminal may be the same or may be different.

In some embodiments, each AI-learning task group corresponds to an identification information that can be used to uniquely identify the AI-learning task group.

For example, each federal learning task group corresponds to an identification information that is used to uniquely identify the federal learning task group or that is used to uniquely identify a federation.

In some embodiments of the present application, the first information directly indicates an AI learning task group to which the first terminal belongs.

That is, the first information may be direct indication information of the AI learning task group to which the first terminal belongs.

For example, the first information is first identification information, which is used to identify an AI learning task group to which the first terminal belongs.

In other words, the first information may be identification information of an AI learning task group to which the first terminal belongs.

In other embodiments of the present application, the first information indirectly indicates an AI learning task group to which the first terminal belongs.

That is, the first information may be indirect instruction information of the AI learning task group to which the first terminal belongs.

For example, the first information may be first configuration information, where the first configuration information corresponds to first identification information one by one, and the first identification information is used to identify an AI learning task group to which the first terminal belongs.

As an example, the first configuration information may be a first relay service code (Relay Service Code, RSC) that corresponds to the first identification information one-to-one, or the first configuration information may be other parameters, which is not limited thereto.

Alternatively, the first identification information or the first AI-learning identification information, and the expressions may be replaced with each other.

In some embodiments of the present application, the first information is or is the first AI operating configuration. The set of AI learning tasks to which the first terminal belongs, or the AI learning task performed, or the AI operation performed, may be determined according to the first AI operation configuration.

In some embodiments of the present application, the second information directly indicates an AI learning task group to which the second terminal belongs.

That is, the second information may be direct indication information of the AI-learning task group to which the second terminal belongs.

For example, the second information is second identification information, which is used to identify the AI learning task group to which the second terminal belongs.

In other words, the second information may be identification information of an AI learning task group to which the second terminal belongs.

In other embodiments of the present application, the second information indirectly indicates an AI learning task group to which the second terminal belongs.

That is, the second information may be indirect instruction information of the AI-learning task group to which the second terminal belongs.

For example, the second information may be second configuration information, where the second configuration information corresponds to second identification information one by one, and the second identification information is used to identify an AI learning task group to which the second terminal belongs.

As an example, the second configuration information may be a second RSC, where the second relay service code corresponds to the second identification information one by one, or the second configuration information may also be other parameters, which is not limited herein.

Alternatively, the second identification information or the second AI-learning identification information, and the expressions may be replaced with each other.

In some embodiments of the present application, the second information or second AI operating configuration. The set of AI-learning tasks to which the second terminal belongs, or the AI-learning task performed, or the AI operation performed, may be determined according to the second AI operation configuration.

In some embodiments of the present application, the core network device may be an existing core network element, for example, a network data analysis function (Network Data Analytics Function, NWDAF) entity, or may also be a newly added core network element, for example, a newly added network element for interfacing with a third party application server, for supporting a third party AI-related requirement in the core network.

In some embodiments, the core network device may be a first core network device, which may be an NWDAF entity or an AI function entity, for supporting AI-related functions. The AI functional entity is a newly added network element.

For example, the AI functionality may send first information to the first terminal for determining a set of AI-learning tasks to which the first terminal belongs.

As a specific example, the AI-functional entity may send first identification information to the first terminal for indicating the AI-learning task group to which the first terminal belongs.

For another example, the AI-function entity may send second information to the second terminal for determining an AI-learning task group to which the second terminal belongs.

As a specific example, the AI-functional entity may send second identification information to the second terminal for indicating the AI-learning task group to which the second terminal belongs.

In other embodiments, the core network device is a second core network device, e.g., the second core network device is a policy control function (Policy Control Function, PCF) entity.

For example, the second core network device may send first information to the first terminal for determining an AI learning task group to which the first terminal belongs.

As a specific example, the second core network device may send first identification information to the first terminal, where the first identification information is used to identify the AI-learning task group to which the first terminal belongs.

As a specific example, the second core network device may send a first RSC to the first terminal, where the first RSC corresponds to first identification information, and the first identification information is used to identify an AI learning task group to which the first terminal belongs.

For example, the second core network device may determine, according to the first identification information, a RSC corresponding to the first identification information, that is, a first RSC, in combination with a correspondence between the AI learning task group and the RSC, and further send the first RSC to the first terminal.

For another example, the second core network device may send second information to the second terminal for determining the AI learning task group to which the second terminal belongs.

As a specific example, the second core network device may send second identification information to the second terminal, where the second identification information is used to identify the AI-learning task group to which the second terminal belongs.

As a specific example, the second core network device may send a second RSC to the second terminal, where the second RSC corresponds to second identification information, and the second identification information is used to identify an AI learning task group to which the second terminal belongs.

For example, the second core network device may determine, according to the second identification information, a RSC corresponding to the second identification information, that is, the second RSC, in combination with a correspondence between the AI learning task group and the RSC, and further send the second RSC to the second terminal.

That is, the second core network device may send the target RSC to the terminal device, where the target RSC corresponds to target identification information for identifying the AI-learning task group to which the terminal device belongs.

In some embodiments, the correspondence between the AI learning task group and the RSC may be preconfigured.

In other words, different RSCs correspond to different AI learning task groups.

For example, different values of some or all bits of the RSC are utilized to indicate different AI learning task groups.

As an example, different types of AI learning tasks are indicated with M bits (e.g., bit 0 and bit 1) in the RSC, e.g., federal learning tasks, migration learning tasks, etc., and different AI learning task groups are indicated with different values of N bits (e.g., bit 2 and bit 3) in the remaining bits in the RSC. For example, if the M bits indicate a federal learning task, the N bits with a value of 0 represent the federal learning task group 0, the N bits with a value of 1 represent the federal learning task group 1, and so on.

It should be understood that, in the embodiment of the present application, the information sent by the core network device to the terminal device is forwarded through the access network device.

In some embodiments, the core network device sending the first information to the first terminal may refer to:

the core network device sends first information to the first terminal through the access network device.

For example, the core network device first sends the first information to the access network device, which further forwards the first information to the first terminal by the access network device.

Similarly, the core network device sending the second information to the second terminal may refer to:

and the core network equipment sends second information to the second terminal through the access network equipment.

For example, the core network device first sends the second information to the access network device, which is further forwarded by the access network device to the second terminal.

In some embodiments of the present application, the first terminal may select a relay terminal according to the first information.

Hereinafter, the first information including the first identification information and/or the first RSC and the second information including the second identification information and/or the second RSC will be described as an example, but the present application is not limited thereto.

In some embodiments, the method 200 further comprises: the first terminal transmits the first identification information and/or the first RSC.

Correspondingly, the other terminals receive the first identification information and/or the first RSC. For example, the second termination receives the first identification information and/or the first RSC.

In some implementations, the first termination transmitting the first identification information and/or the first RSC may include: the first termination sends the first identification information and/or the first RSC in device discovery.

That is, the first terminal may carry in the device discovery the indication information of the AI learning task group that the relay terminal needs to satisfy.

For example, the first terminal transmits a discovery request message (solicitation message) including the first identification information and/or the first RSC.

That is, the first termination may carry the first identification information and/or the first RSC in solicitation message in mode B.

Further, the terminal device that receives the first identification information and/or the first RSC may determine, according to the first identification information and/or the first RSC, whether the terminal device and the first terminal belong to the same AI learning task group in combination with indication information of the AI learning task group (for example, identification information of the AI learning task group, or RSC) configured on the terminal device.

For example, the second terminal may determine whether the first terminal and the second terminal belong to the same AI learning task group according to the first identification information and/or the first RSC combination, and the second identification information and/or the second RSC configured on the second terminal.

For example, the second terminal may determine that the first terminal and the second terminal belong to the same AI learning task group if at least one of the following conditions is satisfied:

the first identification information and the second identification information are the same;

the first RSC is identical to the second RSC.

Further, the terminal device belonging to the same AI learning task group as the first terminal may send a discovery response message to the first terminal.

For example, the second terminal transmits a discovery response message to the first terminal in the case where it is determined that the second terminal belongs to the same AI learning task group as the first terminal.

The first terminal can determine that the second terminal and the first terminal belong to the same AI learning task group according to the discovery response message, and further, the second terminal can be selected as a relay terminal.

In other embodiments, the method 200 further comprises: the second terminal transmits the second identification information and/or the second RSC.

Correspondingly, the first terminal receives the second identification information and/or the second RSC sent by the second terminal.

In some implementations, the second terminal transmitting the second identification information and/or the second RSC may include: the second terminal transmits the second identification information and/or the second RSC in the device discovery.

That is, the second terminal may carry, in the device discovery, the instruction information of the AI learning task group to which the second terminal belongs.

For example, the second terminal transmits a discovery advertisement message (Announcement message) including the second identification information and/or the second RSC therein. That is, the second terminal may transmit the second information in mode a of the proximity service direct discovery (Prose Direct Discovery).

For another example, the second terminal transmits a discovery response message to the first terminal, and the discovery response message includes the second identification information and/or the second RSC.

That is, the second terminal may carry the second identification information and/or the second RSC in the discovery response message in the mode B.

Further, the first terminal may determine, according to the first identification information and/or the first RSC configured on the first terminal, whether the second terminal and the first terminal belong to the same AI learning task group in combination with the second identification information and/or the second RSC sent by the second terminal. In the case where the second terminal and the first terminal belong to the same AI learning task group, the first terminal may select the second terminal as a relay terminal.

For example, the first terminal may determine that the second terminal and the first terminal belong to the same AI learning task group if at least one of the following conditions is satisfied:

the second identification information is the same as the first identification information;

the second RSC is identical to the first RSC.

In summary, whether the first terminal and the second terminal belong to the same AI learning task group may be determined by the first terminal, or may also be determined by the second terminal. For example, the second terminal may acquire the first identification information and/or the first RSC of the first terminal, and then determine whether the first identification information and/or the first RSC and the second information configured on the second terminal belong to the same AI learning task group. For another example, the first terminal may acquire the second identification information and/or the second RSC of the second terminal, and then determine whether the first terminal and the second terminal belong to the same AI learning task group according to the first identification information and/or the first RSC and the first information configured on the first terminal.

In some embodiments of the present application, the first terminal may establish connection with the network through the selected relay terminal, and further send training data or an intermediate training result to the AI server through the selected relay terminal, which is favorable to ensuring smooth performance of the AI learning task and improving performance of the AI model.

That is, the first terminal may be a remote terminal, and may transmit training data or training results to the AI server through the relay terminal in a Prose manner.

For example, due to the problem of calculation power or electric quantity, the first terminal cannot complete all local training, in this case, the first terminal may send the intermediate training result calculated to a certain step or a certain layer to the relay terminal, and because the relay terminal and the first terminal have the same AI model, the relay terminal may train the subsequent layer based on the intermediate training result, and further send the training result to the server corresponding to the AI learning task.

For another example, due to mobility, the first terminal moves out of coverage of the base station or the AI server during the process of performing the local training, in which case, the first terminal may send the intermediate training result calculated to a certain step or a certain layer to the relay terminal, and the relay terminal sends the training result to the AI server.

In some embodiments of the present application, the identification information corresponding to each AI learning task group may be assigned by the core network device.

Optionally, the core network device may be a first core network device, where the first core network device is configured to allocate corresponding identification information for each AI learning task group.

Alternatively, the first core network device may be an existing core network element, such as an NWDAF entity. That is, the function related to the third party AI is supported for the existing core network element.

Optionally, the first core network device may also be a newly added core network element, for example, a newly added AI function entity, for supporting a third party AI-related function in the core network.

It should be understood that, in the embodiment of the present application, the core network device that allocates the identification information corresponding to the AI-learning task group and the core network device that sends the identification information corresponding to the AI-learning task group to the terminal device may be the same core network device, or may also be different core network devices.

For example, after the first core network device allocates the corresponding identification information for the AI-learning task group, the first core network device may further send the identification information to the terminal device in the AI-learning task group.

For another example, after the first core network device allocates the corresponding identification information for the AI learning task group, the identification information may be sent to the second core network device, and the second core network device further sends the identification information to the terminal device in the AI learning task group.

In some embodiments of the present application, the terminal device (e.g., the first terminal and the second terminal) may report the AI operating capabilities supported by itself to the AI server, e.g., whether federal learning is supported. Further, the AI server may determine target terminals constituting the AI-learning task group, or, in other words, target terminals performing the same AI-learning task, according to AI operation capabilities of the terminal device.

In some embodiments, the first core network device may obtain a set of terminals from the AI server that perform the same AI learning task.

For example, the first core network device may obtain, from the federal learning server, a set of terminals that perform the same federal learning task.

Further, the first core network device may send the identification information allocated for the AI-learning task group to the terminal device in the AI-learning task group.

Hereinafter, with reference to fig. 5 to 8, a specific implementation of the method of wireless communication according to the embodiment of the present application will be described by taking an AI learning task as a federal learning task, AI learning identification information as an FL ID, and an AI functional entity as an example, but the present application is not limited thereto.

It should be noted that, in fig. 5 to 8, the remote terminal may correspond to the first terminal in the foregoing, and the relay terminal may correspond to the second terminal in the foregoing. Fig. 5 is a schematic interaction diagram of the generation and configuration process of the AI operation configuration, and fig. 6 to 8 are schematic interaction diagrams of the terminal device selecting a relay terminal according to the AI operation configuration.

As shown in fig. 5, the method may include at least some of the following steps:

s301, the relay terminal reports the AI operation capability information supported by the relay terminal, for example, whether the federal learning task is supported, to the FL server.

S302, the remote terminal reports the AI operation capability information supported by the remote terminal to the FL server, such as whether federal learning tasks are supported or not.

It should be understood that, in the embodiments of the present application, the sequence of reporting the AI operation capability information by the relay terminal and the remote terminal is not limited, and for example, the AI operation capability information may be reported simultaneously, or may be reported by the relay terminal first, or may also be reported by the remote terminal first.

Optionally, the FL server may also receive AI operation capability information reported by more terminals.

S303, the FL server selects a target terminal for executing the federal learning task or forms a federal target terminal according to the AI operation capability information reported by the terminal equipment. The target terminal may be considered to constitute a federal learning task group.

S304, the FL server sends the identification information (such as terminal ID) of the target terminal forming one federation to the AI function entity.

The AI function entity assigns a FL ID to the federal learning task group.

S305, the AI function entity sends the FL ID corresponding to the federal learning task group to the PCF entity.

Optionally, the AI function entity may also send the terminal ID included in the federal learning task group to the PCF entity.

Further, the PCF entity sends AI operating configuration to all terminals in the federal learning task set.

In some embodiments, the AI operating configuration may include a FL ID.

In other embodiments, the AI operating configuration may include an RSC corresponding to the FL ID.

For example, in S306, the PCF entity sends an AI operation configuration to the relay terminal, where the AI operation configuration may include an FL ID allocated by the AI function entity to the federal learning task group to which the relay terminal belongs, or an RSC corresponding to the FL ID allocated by the AI function entity to the federal learning task group to which the relay terminal belongs.

In some implementations, the PCF entity first sends the AI operating configuration of the relay terminal to the access network device, and the access network device further sends the AI operating configuration of the relay terminal to the relay terminal.

For example, in S307, the PCF entity sends an AI operation configuration to the remote terminal, where the AI operation configuration may include an FL ID assigned by the AI function entity to the federal learning task group to which the remote terminal belongs, or an RSC corresponding to the FL ID assigned by the AI function entity to the federal learning task group to which the remote terminal belongs.

In some implementations, the PCF entity first sends the AI operating configuration of the remote terminal to the access network device, and the access network device further sends the AI operating configuration of the remote terminal to the remote terminal.

Alternatively, in some implementations, in S305, the AI function entity may also send the FL ID assigned for the federal learning task group to all terminals in the federal learning task group. For example, the AI-function entity transmits the FL ID to the access network device, and further transmits the FL ID to the corresponding terminal device by the access network device.

In some embodiments of the present application, the remote terminal and the relay terminal may determine whether they are in the same federation according to the AI operation configuration. I.e., the remote terminal may select the relay terminal according to the AI operating configuration. For example, the remote terminal selects the relay terminal to establish a connection with the network only if the AI operating configuration of the relay terminal and the AI operating configuration of the remote terminal are the same.

As shown in fig. 6, the method may include at least some of the following steps:

s311, the relay terminal transmits the AI operation configuration of the relay terminal.

The AI operating configuration herein may be configured in the manner shown in the embodiment of fig. 5.

For example, the relay terminal transmits a second FL ID for identifying the federal learning task group to which the relay terminal belongs.

For another example, the relay terminal transmits a second RSC corresponding to the second FL ID.

Alternatively, the relay terminal may broadcast the AI operating configuration of the relay terminal. For example, the relay terminal may indicate AI operation configuration of the relay terminal in a broadcasted discovery announcement message in mode a of Prose Direct Discovery.

S312, the remote terminal determines whether to select the relay terminal to establish connection with the network according to the AI operation configuration of the remote terminal and the AI operation configuration of the relay terminal.

Optionally, the AI operating configuration of the remote terminal includes a first FL ID and/or a first RSC, where the first RSC corresponds to a first FL ID, and the first FL ID is used to identify a federal learning task group to which the remote terminal belongs.

For example, if the first FL ID and the second FL ID are the same, it is determined to select to establish a connection with the network through the relay terminal.

For another example, if the first RSC and the second RSC are the same, it is determined to select to establish a connection with the network through the relay terminal.

And S313, the remote terminal initiates corresponding connection establishment according to the judgment result.

For example, in the case where the first FL ID and the second FL ID are the same, connection with the network through the relay terminal is selected.

For another example, in case the first RSC and the second RSC are the same, connection with the network is selected to be established through the relay termination.

As shown in fig. 7, the method may include at least some of the following steps:

s321, the remote terminal sends AI operation configuration of the remote terminal.

For example, the remote terminal transmits a first FL ID for identifying the federal learning task group to which the remote terminal belongs.

For another example, the remote terminal transmits a first RSC, which corresponds to the first FL ID.

Alternatively, the remote terminal may carry the AI operating configuration of the remote terminal in the discovery request message.

For example, the remote terminal may indicate an AI operating configuration of the remote terminal, or an AI operating configuration that the relay terminal needs to satisfy, in solicitation message in mode B.

S322, the relay terminal combines the AI operation configuration of the relay terminal according to the AI operation configuration of the remote terminal, and determines whether the two are in the same federation.

Optionally, the AI operation configuration of the relay terminal includes a second FL ID and/or a second RSC, where the second RSC corresponds to a second FL ID, and the second FL ID is used to identify a federal learning task group to which the relay terminal belongs.

For example, if the first FL ID and the second FL ID are the same, it is determined that the remote terminal and the relay terminal are in the same federation.

For another example, if the first RSC and the second RSC are the same, it is determined that the remote termination and the relay termination are in the same federation.

S323, the relay terminal determines whether to send the discovery response message according to the judgment result.

For example, in the case where the first FL ID and the second FL ID are the same, the relay terminal transmits a discovery response message.

For another example, when the first FL ID and the second FL ID are different, the relay terminal does not transmit the discovery response message.

For example, in the case where the first RSC and the second RSC are identical, the relay terminal transmits a discovery response message.

For another example, in case that the first RSC and the second RSC are different, the relay terminal does not transmit the discovery response message.

And S324, the remote terminal initiates corresponding connection establishment according to the discovery response message.

For example, the remote terminal establishes a connection with the network through the relay terminal that transmits the discovery response message.

As shown in fig. 8, the method may include at least some of the following steps:

s331, the remote terminal sends a discovery request message.

In this embodiment, the discovery request message does not carry the AI operating configuration of the remote terminal.

And S332, the relay terminal sends the AI operation configuration of the relay terminal in the discovery response message.

S333, the remote terminal determines whether the relay terminal and the remote terminal are in the same federation according to the AI operation configuration of the remote terminal and the AI operation configuration of the relay terminal.

For example, if the first FL ID and the second FL ID are the same, it is determined that the relay terminal and the remote terminal are in the same federal.

For another example, if the first RSC and the second RSC are the same, it is determined that the relay termination and the remote termination are in the same federal.

And S334, the remote terminal initiates corresponding connection establishment according to the judgment result.

For example, in the case where the first FL ID and the second FL ID are the same, the remote terminal selects to establish a connection with the network through the relay terminal.

For another example, in the case where the first RSC and the second RSC are the same, the remote termination selects to establish a connection with the network through the relay termination.

In summary, in the embodiment of the present application, corresponding identification information is allocated to the AI learning task group through the core network device, so that the remote terminal may determine to select, according to the identification information, a relay terminal belonging to the same AI learning task group to establish connection with the network. Therefore, when the far-end terminal is out of coverage or has poor computing power and low electric quantity, the far-end terminal can send training results or training data to the AI server through the relay terminal, so that the AI learning task can be smoothly carried out. In addition, through distributing corresponding identification information for the AI learning task group, only terminals belonging to the same AI learning task group are needed to be searched through the identification information, specific model information is not needed to be provided, and training model information owned by terminal equipment can be effectively protected.

The method embodiments of the present application are described in detail above with reference to fig. 4 to 8, and the apparatus embodiments of the present application are described in detail below with reference to fig. 9 to 14, it being understood that the apparatus embodiments and the method embodiments correspond to each other, and similar descriptions may refer to the method embodiments.

Fig. 9 shows a schematic block diagram of a terminal device 400 according to an embodiment of the present application. As shown in fig. 9, the terminal apparatus 400 includes:

and the communication unit 410 is configured to receive first information sent by the core network device, where the first information is used to determine an artificial intelligence AI learning task group to which the terminal device belongs.

In some embodiments of the present application, the first information includes at least one of:

the terminal equipment comprises first AI learning identification information and first relay service codes, wherein the first AI learning identification information is used for identifying an AI learning task group to which the terminal equipment belongs, and the first relay service codes correspond to the first AI learning identification information.

In some embodiments of the present application, the communication unit 410 is further configured to:

and sending the first AI learning identification information and/or the first relay service code.

And sending a discovery request message, wherein the discovery request message comprises the first AI learning identification information and/or the first relay service code.

and receiving a discovery response message sent by a second terminal, wherein the second terminal and the terminal equipment belong to the same AI learning task group.

In some embodiments of the present application, the terminal device further includes:

and the processing unit is used for selecting the second terminal as a relay terminal.

and receiving second information sent by a second terminal, wherein the second information is used for determining an AI learning task group to which the second terminal belongs.

In some embodiments of the present application, the second information includes at least one of:

the system comprises a second terminal and second AI learning identification information and second relay service codes, wherein the second AI learning identification information is used for identifying an AI learning task group to which the second terminal belongs, and the second relay service codes correspond to the second AI learning identification information.

and receiving a discovery announcement message sent by the second terminal, wherein the discovery announcement message comprises the second information.

and receiving a discovery response message sent by the second terminal, wherein the discovery response message comprises the second information.

and the processing unit is used for determining whether to select the second terminal as a relay terminal according to the second information and the first information.

In some embodiments of the present application, the processing unit is further configured to:

determining whether the second terminal and the terminal equipment belong to the same AI learning task group according to the second information and the first information;

and selecting the second terminal as a relay terminal under the condition that the second terminal and the terminal equipment belong to the same AI learning task group.

determining that the second terminal and the terminal device belong to the same AI learning task group if at least one of the following conditions is satisfied:

the AI learning identification information included in the second information is the same as the AI learning identification information included in the first information;

the relay service code included in the second information is the same as the relay service code included in the first information.

In some embodiments of the present application, the core network device includes a first core network device, where the first core network device is an artificial intelligence AI functional entity.

In some embodiments of the present application, the core network device includes a second core network device, where the second core network device is a policy control function PCF entity.

and sending training data of the AI model to a server corresponding to a first AI learning task through a second terminal, wherein the terminal equipment and the second terminal belong to an AI learning task group corresponding to the first AI learning task.

In some embodiments of the present application, the first information includes first AI-learning identification information, where the first AI-learning identification information is allocated by an AI functional entity to an AI-learning task group to which the terminal device belongs.

In some embodiments of the present application, the AI-learning task group is a federal learning task group, the first information includes first AI-learning identification information, and the first AI-learning identification information is federal learning identification information.

Alternatively, in some embodiments, the communication unit may be a communication interface or transceiver, or an input/output interface of a communication chip or a system on a chip. The processing unit may be one or more processors.

It should be understood that the terminal device 400 according to the embodiment of the present application may correspond to the first terminal in the embodiment of the method of the present application, and the foregoing and other operations and/or functions of each unit in the terminal device 400 are respectively for implementing the corresponding flow of the first terminal in the method shown in fig. 4 to 8, and are not repeated herein for brevity.

Fig. 10 shows a schematic block diagram of a terminal device 500 according to an embodiment of the present application. As shown in fig. 10, the terminal device 500 includes:

and a communication unit 510, configured to receive second information sent by a core network device, where the second information is used to determine an AI learning task group to which the terminal device belongs.

In some embodiments of the present application, the communication unit 510 is further configured to:

and receiving first information sent by a first terminal, wherein the first information is used for determining an AI learning task group to which the first terminal belongs.

and receiving a discovery request message sent by the first terminal, wherein the discovery request message comprises the first information.

the processing unit is used for determining whether the first terminal and the terminal equipment belong to the same AI learning task group according to the first information and the second information;

the communication unit 510 is further configured to: and sending a discovery response message to the first terminal under the condition that the first terminal and the terminal equipment belong to the same AI learning task group.

determining that the first terminal and the terminal device belong to the same AI learning task group if at least one of the following conditions is satisfied:

the AI learning identification information included in the first information is the same as the AI learning identification information included in the second information;

the relay service code included in the first information is the same as the relay service code included in the second information.

In some embodiments of the present application, the communication unit 510 is further configured to: and sending the second AI learning identification information and/or the second relay service code.

and sending a discovery announcement message, wherein the discovery announcement message comprises the second AI learning identification information and/or the second relay service code.

and sending a discovery response message to the first terminal, wherein the discovery response message comprises the second AI learning identification information and/or the second relay service code.

In some embodiments of the present application, the second information includes second AI-learning identification information, where the second AI-learning identification information is allocated by an AI functional entity for an AI-learning task group to which the terminal device belongs.

In some embodiments of the present application, the AI-learning task group is a federal learning task group, and the second information includes second AI-learning identification information, where the second AI-learning identification information is federal learning identification information.

It should be understood that the terminal device 500 according to the embodiment of the present application may correspond to the second terminal in the embodiment of the method of the present application, and the foregoing and other operations and/or functions of each unit in the terminal device 500 are respectively for implementing the corresponding flow of the second terminal in the method shown in fig. 4 to 8, which are not repeated herein for brevity.

Fig. 11 is a schematic block diagram of a core network device according to an embodiment of the present application. The core network device 800 of fig. 11 includes:

a communication unit 810, configured to obtain identification information of a terminal device in the first artificial intelligence AI learning task group;

a processing unit 820, configured to assign first AI-learning identification information to the first AI-learning task group, where the first AI-learning identification information is used to identify the first AI-learning task group;

the communication unit 810 is further configured to: and sending the first AI learning identification information to the terminal equipment in the first AI learning task group.

In some embodiments of the present application, the communication unit 810 is further configured to:

And sending the first AI learning identification information to the terminal equipment in the first AI learning task group through second core network equipment.

In some embodiments of the present application, the second core network device is a policy control function PCF entity.

In some embodiments of the present application, the core network device is an artificial intelligence AI functional entity.

and acquiring the identification information of the terminal equipment in the first AI learning task group from the server corresponding to the first AI learning task.

It should be understood that the core network device 800 according to the embodiment of the present application may correspond to a core network device, such as the first core network device, in the embodiment of the method of the present application, and the foregoing and other operations and/or functions of each unit in the core network device 800 are respectively for implementing the corresponding flows of the core network device in the method shown in fig. 4 to 8, which are not described herein for brevity.

Fig. 12 is a schematic block diagram of a core network device according to an embodiment of the present application. The core network apparatus 1000 of fig. 12 includes:

and a communication unit 1010, configured to send a first relay service code to a first terminal, where the first relay service code corresponds to first artificial intelligence AI learning identification information, and the first AI learning identification information is used to identify an AI learning task group to which the first terminal belongs.

In some embodiments of the present application, the core network device further includes:

and the processing unit is used for determining the first relay service code according to the corresponding relation of the first AI learning identification information and the relay service code by combining the AI learning identification information.

In some embodiments of the present application, the correspondence between the AI-learning identification information and the relay service code is preconfigured.

In some embodiments of the present application, the core network device is a policy control function PCF entity.

It should be understood that the core network device 1000 according to the embodiments of the present application may correspond to a core network device, such as a second core network device, in the embodiments of the methods of the present application, and that the foregoing and other operations and/or functions of each unit in the core network device 1000 are respectively for implementing the corresponding flows of the core network device in the methods shown in fig. 4 to 8, and are not described herein for brevity.

Fig. 13 is a schematic structural diagram of a communication device 600 provided in an embodiment of the present application. The communication device 600 shown in fig. 13 comprises a processor 610, from which the processor 610 may call and run a computer program to implement the method in the embodiments of the present application.

Optionally, as shown in fig. 13, the communication device 600 may further comprise a memory 620. Wherein the processor 610 may call and run a computer program from the memory 620 to implement the methods in embodiments of the present application.

The memory 620 may be a separate device from the processor 610 or may be integrated into the processor 610.

Optionally, as shown in fig. 13, the communication device 600 may further include a transceiver 630, and the processor 610 may control the transceiver 630 to communicate with other devices, and in particular, may send information or data to other devices, or receive information or data sent by other devices.

The transceiver 630 may include a transmitter and a receiver, among others. Transceiver 630 may further include antennas, the number of which may be one or more.

Optionally, the communication device 600 may be specifically a core network device in the embodiment of the present application, and the communication device 600 may implement a corresponding flow implemented by the core network device in each method in the embodiment of the present application, which is not described herein for brevity.

Optionally, the communication device 600 may specifically be the first terminal in the embodiment of the present application, and the communication device 600 may implement a corresponding flow implemented by the first terminal in each method in the embodiment of the present application, which is not described herein for brevity.

Optionally, the communication device 600 may specifically be the second terminal in the embodiment of the present application, and the communication device 600 may implement a corresponding flow implemented by the second terminal in each method in the embodiment of the present application, which is not described herein for brevity.

Fig. 14 is a schematic structural diagram of a chip of an embodiment of the present application. The chip 700 shown in fig. 14 includes a processor 710, and the processor 710 may call and run a computer program from a memory to implement the method in the embodiments of the present application.

Optionally, as shown in fig. 14, chip 700 may also include memory 720. Wherein the processor 710 may call and run a computer program from the memory 720 to implement the methods in embodiments of the present application.

Wherein the memory 720 may be a separate device from the processor 710 or may be integrated into the processor 710.

Optionally, the chip 700 may also include an input interface 730. The processor 710 may control the input interface 730 to communicate with other devices or chips, and in particular, may obtain information or data sent by other devices or chips.

Optionally, the chip 700 may further include an output interface 740. The processor 710 may control the output interface 740 to communicate with other devices or chips, and in particular, may output information or data to other devices or chips.

Optionally, the chip may be applied to the core network device in the embodiment of the present application, and the chip may implement a corresponding flow implemented by the core network device in each method in the embodiment of the present application, which is not described herein for brevity.

Optionally, the chip may be applied to the first terminal in the embodiment of the present application, and the chip may implement a corresponding flow implemented by the first terminal in each method in the embodiment of the present application, which is not described herein for brevity.

Optionally, the chip may be applied to the second terminal in the embodiment of the present application, and the chip may implement a corresponding flow implemented by the second terminal in each method in the embodiment of the present application, which is not described herein for brevity.

It should be understood that the chips referred to in the embodiments of the present application may also be referred to as system-on-chip chips, or the like.

Fig. 15 is a schematic block diagram of a communication system 1100 provided by an embodiment of the present application. As shown in fig. 15, the communication system 1100 includes a far-end terminal 1110, a relay terminal 1120, and a core network device 1130.

The remote terminal 1110 may be used to implement the corresponding function implemented by the first terminal in the above method, the relay terminal 1120 may be used to implement the corresponding function implemented by the second terminal in the above method, and the core network device 1130 may be used to implement the corresponding function implemented by the core network device (e.g., the first core network device and/or the second core network device) in the above method, which are not described herein for brevity.

It should be appreciated that the processor of an embodiment of the present application may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method embodiments may be implemented by integrated logic circuits of hardware in a processor or instructions in software form. The processor may be a general purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), an off-the-shelf programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in hardware, in a decoded processor, or in a combination of hardware and software modules in a decoded processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.

It will be appreciated that the memory in embodiments of the present application may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable EPROM (EEPROM), or a flash Memory. The volatile memory may be random access memory (Random Access Memory, RAM) which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (Double Data Rate SDRAM), enhanced SDRAM (ESDRAM), synchronous DRAM (SLDRAM), and Direct RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.

It should be understood that the above memory is exemplary but not limiting, and for example, the memory in the embodiments of the present application may be Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), direct RAM (DR RAM), and the like. That is, the memory in embodiments of the present application is intended to comprise, without being limited to, these and any other suitable types of memory.

Embodiments of the present application also provide a computer-readable storage medium for storing a computer program.

Optionally, the computer readable storage medium may be applied to a core network device in the embodiments of the present application, for example, a first core network device, a second core network device, and the computer program causes a computer to execute a corresponding procedure implemented by the core network device in each method in the embodiments of the present application, which is not described herein for brevity.

Optionally, the computer readable storage medium may be applied to the first terminal in the embodiments of the present application, and the computer program causes a computer to execute a corresponding flow implemented by the first terminal in each method of the embodiments of the present application, which is not described herein for brevity.

Optionally, the computer readable storage medium may be applied to the second terminal in the embodiments of the present application, and the computer program causes a computer to execute a corresponding flow implemented by the second terminal in each method of the embodiments of the present application, which is not described herein for brevity.

Embodiments of the present application also provide a computer program product comprising computer program instructions.

Optionally, the computer program product may be applied to a core network device in the embodiment of the present application, for example, the first core network device, the second core network device, and the computer program instructions cause the computer to execute corresponding processes implemented by the core network device in each method in the embodiment of the present application, which is not described herein for brevity.

Optionally, the computer program product may be applied to the first terminal in the embodiments of the present application, and the computer program instructions cause the computer to execute a corresponding procedure implemented by the first terminal in each method of the embodiments of the present application, which is not described herein for brevity.

Optionally, the computer program product may be applied to the second terminal in the embodiments of the present application, and the computer program instructions cause the computer to execute a corresponding procedure implemented by the second terminal in each method of the embodiments of the present application, which is not described herein for brevity.

The embodiment of the application also provides a computer program.

Optionally, the computer program may be applied to a core network device in the embodiments of the present application, for example, the first core network device and the second core network device, and when the computer program runs on a computer, the computer is caused to execute corresponding processes implemented by the core network device in each method in the embodiments of the present application, which are not described herein for brevity.

Optionally, the computer program may be applied to the first terminal in the embodiments of the present application, and when the computer program runs on a computer, the computer is caused to execute a corresponding flow implemented by the first terminal in each method in the embodiments of the present application, which is not described herein for brevity.

Optionally, the computer program may be applied to the second terminal in the embodiments of the present application, and when the computer program runs on a computer, the computer is caused to execute a corresponding flow implemented by the second terminal in each method in the embodiments of the present application, which is not described herein for brevity.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.

In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

A method of wireless communication, comprising:

and the first terminal receives first information sent by core network equipment, wherein the first information is used for determining an artificial intelligence AI learning task group to which the first terminal belongs.
The method of claim 1, wherein the first information comprises at least one of:

the system comprises first AI learning identification information and first relay service codes, wherein the first AI learning identification information is used for identifying an AI learning task group to which the first terminal belongs, and the first relay service codes correspond to the first AI learning identification information.
The method according to claim 2, wherein the method further comprises:

and the first terminal sends the first AI learning identification information and/or the first relay service code.
The method of claim 3, wherein the first terminal transmitting the first AI-learning identification information and/or the first relay service code comprises:

the first terminal sends a discovery request message, wherein the discovery request message comprises the first AI learning identification information and/or the first relay service code.
The method according to claim 3 or 4, characterized in that the method further comprises:

the first terminal receives a discovery response message sent by a second terminal, and the second terminal and the first terminal belong to the same AI learning task group.
The method of claim 5, wherein the method further comprises:

the first terminal selects the second terminal as a relay terminal.
The method according to claim 1 or 2, characterized in that the method further comprises:

the first terminal receives second information sent by a second terminal, wherein the second information is used for determining an AI learning task group to which the second terminal belongs.
The method of claim 7, wherein the second information comprises at least one of:

the system comprises a second terminal and second AI learning identification information and second relay service codes, wherein the second AI learning identification information is used for identifying an AI learning task group to which the second terminal belongs, and the second relay service codes correspond to the second AI learning identification information.
The method of claim 8, wherein the first terminal receiving the second information sent by the second terminal comprises:

the first terminal receives a discovery announcement message sent by the second terminal, wherein the discovery announcement message comprises the second information.
The method of claim 8, wherein the first terminal receiving the second information sent by the second terminal comprises:

the first terminal receives a discovery response message sent by the second terminal, wherein the discovery response message comprises the second information.
The method according to any one of claims 7-10, further comprising:

and the first terminal determines whether to select the second terminal as a relay terminal according to the second information and the first information.
The method of claim 11, wherein the first terminal determining whether to select the second terminal as a relay terminal based on the second information and the first information comprises:

the first terminal determines whether the second terminal and the first terminal belong to the same AI learning task group according to the second information and the first information;

And selecting the second terminal as a relay terminal under the condition that the second terminal and the first terminal belong to the same AI learning task group.
The method of claim 12, wherein the first terminal determining whether the second terminal and the first terminal belong to the same AI learning task group based on the second information or the first information comprises:

determining that the second terminal and the first terminal belong to the same AI learning task group if at least one of the following conditions is satisfied:

the AI learning identification information included in the second information is the same as the AI learning identification information included in the first information;

the relay service code included in the second information is the same as the relay service code included in the first information.
The method according to any of claims 1-13, wherein the core network device comprises a first core network device, the first core network device being a network data analysis function, NWDAF, entity or an artificial intelligence, AI, function entity.
The method according to any of claims 1-13, wherein the core network device comprises a second core network device, the second core network device being a policy control function, PCF, entity.
The method according to any one of claims 1-15, further comprising:

the first terminal sends training data of an AI model to a server corresponding to a first AI learning task through a second terminal, wherein the first terminal and the second terminal both belong to an AI learning task group corresponding to the first AI learning task.
The method of any of claims 1-16, wherein the first information includes first AI-learning identification information that is assigned by an AI-functional entity for a set of AI-learning tasks to which the first terminal belongs.
The method of any of claims 1-17, wherein the AI-learning task group is a federal learning task group, the first information includes first AI-learning identification information, and the first AI-learning identification information is federal learning identification information.
A method of wireless communication, comprising:

and the second terminal receives second information sent by the core network equipment, wherein the second information is used for determining an AI learning task group to which the second terminal belongs.
The method of claim 19, wherein the second information comprises at least one of:

The system comprises a second terminal and second AI learning identification information and second relay service codes, wherein the second AI learning identification information is used for identifying an AI learning task group to which the second terminal belongs, and the second relay service codes correspond to the second AI learning identification information.
The method according to claim 19 or 20, characterized in that the method further comprises:

and the second terminal receives first information sent by the first terminal, wherein the first information is used for determining an AI learning task group to which the first terminal belongs.
The method of claim 21, wherein the second terminal receiving the first information sent by the first terminal comprises:

the second terminal receives a discovery request message sent by the first terminal, wherein the discovery request message comprises the first information.
The method according to claim 21 or 22, characterized in that the method further comprises:

the second terminal determines whether the first terminal and the second terminal belong to the same AI learning task group according to the first information and the second information;

and sending a discovery response message to the first terminal under the condition that the first terminal and the second terminal belong to the same AI learning task group.
The method of claim 23, wherein the second terminal determining whether the first terminal and the second terminal belong to the same AI learning task group based on the first information and the second information comprises:

the second terminal determines that the first terminal and the second terminal belong to the same AI learning task group if at least one of the following conditions is satisfied:

the AI learning identification information included in the first information is the same as the AI learning identification information included in the second information;

the relay service code included in the first information is the same as the relay service code included in the second information.
The method of claim 20, wherein the method further comprises:

and the second terminal sends the second AI learning identification information and/or the second relay service code.
The method of claim 25, wherein the second terminal transmitting the second information comprises:

and the second terminal sends a discovery announcement message, wherein the discovery announcement message comprises the second AI learning identification information and/or the second relay service code.
The method of claim 25, wherein the second terminal transmitting second AI-learning identification information and/or the second relay service code comprises:

And the second terminal sends a discovery response message to the first terminal, wherein the discovery response message comprises the second AI learning identification information and/or the second relay service code.
The method according to any of the claims 19-27, wherein the core network device comprises a first core network device being a network data analysis function, NWDAF, entity or an artificial intelligence, AI, function entity.
The method according to any of claims 19-27, wherein the core network device comprises a second core network device, the second core network device being a policy control function, PCF, entity.
The method of any of claims 19-29, wherein the second information includes second AI-learning identification information, wherein the second AI-learning identification information is assigned by an NWDAF entity or an AI-functional entity for a group of AI-learning tasks to which the second terminal belongs.
The method of any of claims 19-30, wherein the AI-learning task group is a federal learning task group, the second information includes second AI-learning identification information, and the second AI-learning identification information is federal learning identification information.
A method of wireless communication, comprising:

the first core network equipment acquires identification information of terminal equipment in a first artificial intelligent AI learning task group;

the first core network device distributes first AI learning identification information for the first AI learning task group, wherein the first AI learning identification information is used for identifying the first AI learning task group;

and the first core network equipment sends the first AI learning identification information to the terminal equipment in the first AI learning task group.
The method of claim 32, wherein the method further comprises:

and the first core network equipment sends the first AI learning identification information to the terminal equipment in the first AI learning task group through the second core network equipment.
The method of claim 33 wherein the second core network device is a policy control function, PCF, entity.
The method according to any of claims 32-34, wherein the first core network device is a network data analysis function, NWDAF, entity or an artificial intelligence, AI, function entity.
The method according to any one of claims 32-35, wherein the first core network device obtaining identification information of a terminal device in a first AI learning task group includes:

The first core network device acquires identification information of terminal devices in the first AI learning task group from a server corresponding to the first AI learning task.
A method of wireless communication, comprising:

the second core network device sends a first relay service code to the first terminal, wherein the first relay service code corresponds to first artificial intelligence AI learning identification information, and the first AI learning identification information is used for identifying an AI learning task group to which the first terminal belongs.
The method of claim 37, wherein the method further comprises: and the second core network equipment determines the first relay service code according to the corresponding relation of the first AI learning identification information and the relay service code.
The method of claim 38, wherein the correspondence of AI-learning identification information and relay service codes is preconfigured.
The method according to any of claims 37-39, wherein the second core network device is a policy control function, PCF, entity.
A terminal device, comprising:

the communication unit is used for receiving first information sent by the core network equipment, wherein the first information is used for determining an artificial intelligence AI learning task group to which the terminal equipment belongs.
The terminal device of claim 41, wherein the first information includes at least one of:

the terminal equipment comprises first AI learning identification information and first relay service codes, wherein the first AI learning identification information is used for identifying an AI learning task group to which the terminal equipment belongs, and the first relay service codes correspond to the first AI learning identification information.
The terminal device of claim 42, wherein the communication unit is further configured to:

and sending the first AI learning identification information and/or the first relay service code.
The terminal device of claim 43, wherein the communication unit is further configured to:

and sending a discovery request message, wherein the discovery request message comprises the first AI learning identification information and/or the first relay service code.
The terminal device of claim 43 or 44, wherein the communication unit is further configured to:

and receiving a discovery response message sent by a second terminal, wherein the second terminal and the terminal equipment belong to the same AI learning task group.
The terminal device of claim 45, wherein the terminal device further comprises:

And the processing unit is used for selecting the second terminal as a relay terminal.
The terminal device according to claim 41 or 42, wherein the communication unit is further configured to:

and receiving second information sent by a second terminal, wherein the second information is used for determining an AI learning task group to which the second terminal belongs.
The terminal device of claim 47, wherein the second information includes at least one of:

the system comprises a second terminal and second AI learning identification information and second relay service codes, wherein the second AI learning identification information is used for identifying an AI learning task group to which the second terminal belongs, and the second relay service codes correspond to the second AI learning identification information.
The terminal device of claim 48, wherein the communication unit is further configured to:

and receiving a discovery announcement message sent by the second terminal, wherein the discovery announcement message comprises the second information.
The terminal device of claim 49, wherein the communication unit is further configured to:

and receiving a discovery response message sent by the second terminal, wherein the discovery response message comprises the second information.
The terminal device according to any of the claims 47-50, characterized in that the terminal device further comprises:

and the processing unit is used for determining whether to select the second terminal as a relay terminal according to the second information and the first information.
The terminal device of claim 51, wherein the processing unit is further configured to:

determining whether the second terminal and the terminal equipment belong to the same AI learning task group according to the second information and the first information;

and selecting the second terminal as a relay terminal under the condition that the second terminal and the terminal equipment belong to the same AI learning task group.
The terminal device of claim 52, wherein the processing unit is further configured to:

determining that the second terminal and the terminal device belong to the same AI learning task group if at least one of the following conditions is satisfied:

the AI learning identification information included in the second information is the same as the AI learning identification information included in the first information;

the relay service code included in the second information is the same as the relay service code included in the first information.
The terminal device according to any of the claims 41-53, characterized in that the core network device comprises a first core network device being a network data analysis function NWDAF entity or an artificial intelligence AI function entity.
The terminal device according to any of the claims 41-54, wherein the core network device comprises a second core network device, the second core network device being a policy control function, PCF, entity.
The terminal device according to any of the claims 41-55, wherein the communication unit is further adapted to:

and sending training data of the AI model to a server corresponding to a first AI learning task through a second terminal, wherein the terminal equipment and the second terminal belong to an AI learning task group corresponding to the first AI learning task.
The terminal device of any of claims 41-56, wherein the first information includes first AI-learning identity information that is assigned by an NWDAF entity or an AI-functional entity for a group of AI-learning tasks to which the terminal device belongs.
The terminal device of any of claims 41-57, wherein the AI-learning task group is a federal learning task group, the first information includes first AI-learning identification information, and the first AI-learning identification information is federal learning identification information.
A terminal device, comprising:

and the communication unit is used for receiving second information sent by the core network equipment, wherein the second information is used for determining an AI learning task group to which the terminal equipment belongs.
The terminal device of claim 59, wherein the second information includes at least one of:

the terminal equipment comprises first AI learning identification information and first relay service codes, wherein the first AI learning identification information is used for identifying an AI learning task group to which the terminal equipment belongs, and the first relay service codes correspond to the first AI learning identification information.
The terminal device of claim 59 or 60, wherein the communication unit is further configured to:

and receiving first information sent by a first terminal, wherein the first information is used for determining an AI learning task group to which the first terminal belongs.
The terminal device of claim 61, wherein the communication unit is further configured to:

and receiving a discovery request message sent by the first terminal, wherein the discovery request message comprises the first information.
A terminal device according to claim 61 or 62, characterized in that the terminal device further comprises:

The processing unit is used for determining whether the first terminal and the terminal equipment belong to the same AI learning task group according to the first information and the second information;

the communication unit is further configured to: and sending a discovery response message to the first terminal under the condition that the first terminal and the terminal equipment belong to the same AI learning task group.
The terminal device of claim 63, wherein the processing unit is further configured to:

determining that the first terminal and the terminal device belong to the same AI learning task group if at least one of the following conditions is satisfied:

the AI learning identification information included in the first information is the same as the AI learning identification information included in the second information;

the relay service code included in the first information is the same as the relay service code included in the second information.
The terminal device of claim 60, wherein the communication unit is further configured to:

and sending the second AI learning identification information and/or the second relay service code.
The terminal device of claim 65, wherein the communication unit is further configured to:

and sending a discovery announcement message, wherein the discovery announcement message comprises the second AI learning identification information and/or the second relay service code.
The terminal device of claim 65, wherein the communication unit is further configured to:

and sending a discovery response message to the first terminal, wherein the discovery response message comprises the second AI learning identification information and/or the second relay service code.
The terminal device according to any of the claims 59-67, characterized in that the core network device comprises a first core network device being a network data analysis function NWDAF entity or an artificial intelligence AI function entity.
The terminal device of any of claims 59-67, wherein the core network device comprises a second core network device, the second core network device being a policy control function, PCF, entity.
The terminal device of any of claims 59-69, wherein the second information includes second AI-learning identity information, wherein the second AI-learning identity information is assigned by an NWDAF entity or an AI-functional entity for a group of AI-learning tasks to which the terminal device belongs.
The terminal device of any of claims 59-70, wherein the AI-learning task group is a federal learning task group, and the second information includes second AI-learning identification information, and wherein the second AI-learning identification information is federal learning identification information.
A core network device, comprising:

the communication unit is used for acquiring the identification information of the terminal equipment in the first artificial intelligent AI learning task group;

the processing unit is used for distributing first AI learning identification information to the first AI learning task group, wherein the first AI learning identification information is used for identifying the first AI learning task group;

the communication unit is further configured to: and sending the first AI learning identification information to the terminal equipment in the first AI learning task group.
The core network device of claim 72, wherein the communication unit is further configured to:

and sending the first AI learning identification information to the terminal equipment in the first AI learning task group through second core network equipment.
The core network device of claim 73, wherein the second core network device is a policy control function, PCF, entity.
The core network device according to any of the claims 72-74, characterized in that the core network device is a network data analysis function NWDAF entity or an artificial intelligence AI function entity.
The core network device of any of claims 72-75, wherein the communication unit is further configured to:

And acquiring the identification information of the terminal equipment in the first AI learning task group from the server corresponding to the first AI learning task.
A core network device, comprising:

the communication unit is used for sending a first relay service code to the first terminal, wherein the first relay service code corresponds to first artificial intelligence AI learning identification information, and the first AI learning identification information is used for identifying an AI learning task group to which the first terminal belongs.
The core network device of claim 77, wherein said core network device further comprises:

and the processing unit is used for determining the first relay service code according to the corresponding relation of the first AI learning identification information and the relay service code by combining the AI learning identification information.
The core network device of claim 78, wherein the correspondence of AI-learning identification information and relay service codes is preconfigured.
The core network device of any of claims 77-79, wherein the core network device is a policy control function, PCF, entity.
A terminal device, comprising: a processor and a memory for storing a computer program, the processor being for invoking and running the computer program stored in the memory, performing the method of any of claims 1 to 18.
A chip, comprising: a processor for calling and running a computer program from a memory, causing a device on which the chip is mounted to perform the method of any one of claims 1 to 18.
A computer readable storage medium storing a computer program for causing a computer to perform the method of any one of claims 1 to 18.
A computer program product comprising computer program instructions for causing a computer to perform the method of any one of claims 1 to 18.
A computer program, characterized in that the computer program causes a computer to perform the method according to any one of claims 1 to 18.
A terminal device, comprising: a processor and a memory for storing a computer program, the processor being for invoking and running the computer program stored in the memory, performing the method of any of claims 19 to 31.
A chip, comprising: a processor for calling and running a computer program from a memory, causing a device on which the chip is mounted to perform the method of any of claims 19 to 31.
A computer readable storage medium storing a computer program for causing a computer to perform the method of any one of claims 19 to 31.
A computer program product comprising computer program instructions which cause a computer to perform the method of any of claims 19 to 31.
A computer program, characterized in that the computer program causes a computer to perform the method of any one of claims 19 to 31.
A core network device, comprising: a processor and a memory for storing a computer program, the processor being for invoking and running the computer program stored in the memory, performing the method of any of claims 32 to 36.
A chip, comprising: a processor for calling and running a computer program from a memory, causing a device on which the chip is mounted to perform the method of any of claims 32 to 36.
A computer readable storage medium storing a computer program for causing a computer to perform the method of any one of claims 32 to 36.
A computer program product comprising computer program instructions which cause a computer to perform the method of any of claims 32 to 36.
A computer program, characterized in that the computer program causes a computer to perform the method of any of claims 32 to 36.
A core network device, comprising: a processor and a memory for storing a computer program, the processor being for invoking and running the computer program stored in the memory, performing the method of any of claims 37 to 40.
A chip, comprising: a processor for calling and running a computer program from a memory, causing a device on which the chip is mounted to perform the method of any one of claims 37 to 40.
A computer readable storage medium storing a computer program for causing a computer to perform the method of any one of claims 37 to 40.
A computer program product comprising computer program instructions for causing a computer to perform the method of any one of claims 37 to 40.
A computer program, characterized in that the computer program causes a computer to perform the method of any one of claims 37 to 40.