Abstract: A model supervision processing method and apparatus, a network-side device, and a readable storage medium. The model supervision processing method of embodiments of this application includes: receiving, by a first network device, first information from a second network device, where the first information includes a data analysis result; and sending, by the first network device, second information to the second network device or a third network device, where the second information is used to indicate a usage status of the data analysis result by the first network device.

Abstract: This application provides a communication method, a communication apparatus, a device, and a communication system. A data analytics network element determines a service quality analytics result for a target terminal device, and sends the service quality analytics result to an access network element. After receiving the service quality analytics result, the access network element may perform handover decision on the target terminal device based on the service quality analytics result.

Abstract: A communication method includes: sending, by a policy control network element, first query information to a data analytics network element, where the first query information is used to obtain service quality information; and receiving, by the policy control network element, first response information sent by the data analytics network element, where the first response information includes the quality information that is of the service and that is requested using the first query information.

Abstract: Embodiments of this application provide a terminal information processing method for determining exception information of a terminal. The method includes: obtaining, by a data analytics network element, data of a first terminal on an application function network element, where the data includes exception information of the first terminal; obtaining, by the data analytics network element, first terminal information of the first terminal on one or more network elements; and determining, by the data analytics network element, exception information of a second terminal based on the exception information of the first terminal and the first terminal information.

Abstract: The present disclosure relates to information processing methods, apparatus, and systems. In one example method, a first network element receives data analytics information from a first data analytics network element, where the data analytics information includes one or more of an identifier of a first terminal, activity degree information of the first terminal, moving trajectory information of the first terminal, and first risk information of the first terminal. The first network element determines an association relationship between the first terminal and an emergency event based on the data analytics information.

Abstract: A policy determining method is disclosed, and includes: obtaining, based on a digital twin entity, first data and a label corresponding to the first data; and training, based on the first data and the label corresponding to the first data, to obtain an artificial intelligence AI model. The AI model is configured to obtain an air interface access policy and/or a user plane path selection policy of a plurality of terminal devices in a network or in an area. This helps resolve a problem of the air interface access policy and user plane path selection of the plurality of terminal devices in the network or in the area. This can further ensure that network energy consumption is the lowest when overall average service experience of the terminal devices reaches a specific threshold.

Abstract: This application provides a communication processing method and apparatus, a communication device, and a readable storage medium. The communication processing method includes: receiving, by a terminal, a first parameter from a first core network device; and establishing, by the terminal, the first service based on the first parameter.

Abstract: This application provides a model training method and apparatus. The method includes: obtaining first capability information and second capability information of a client; determining, based on the first capability information and the second capability information, that the client reports model information to a cloud server or an edge server; and sending first indication information to the client. The first indication information indicates the client to report the model information to the cloud server or the edge server. According to the model training method and apparatus provided in this application, in a horizontal federated training method in a hybrid mode, a manner of reporting local model information by the client may be flexibly selected based on capability information of the client, so that horizontal federated training efficiency can be improved.

Abstract: A communication method includes sending, by a first data analytics network element, a first request to a service discovery network element. The first request requests information about a second data analytics network element. The first request includes one or more of information about distributed learning or first indication information. The information about distributed learning includes a type of distributed learning. The first indication information indicates a type of the second data analytics network element. The method also includes receiving, by the first data analytics network element, information about the second data analytics network element from the service discovery network element. The second data analytics network element supports the type of distributed learning.

Abstract: A session management method, an access management function device, and a session management device, where the method is carried out by an access and mobility management network element and includes: obtaining first subscription data of a terminal in a first communications network and second subscription data of the terminal in a second communications network, where the first subscription data includes a data network name (DNN), and the second subscription data includes an access point name (APN) corresponding to the DNN; allocating a bearer identifier to a bearer in a packet data network (PDN) connection corresponding to the APN; receiving a flow identifier that is of a flow in a protocol data unit (PDU) session corresponding to the DNN and that is sent by a session management network element; and sending the bearer identifier and the flow identifier to the terminal.

Abstract: A model selection method includes recording, by a first network element, model information of N candidate models. The N candidate models are applicable to a data analysis task corresponding to a first analytic ID; receiving, by the first network element, a first model request message from a second network element; and sending, by the first network element in a case that model information of M candidate models of the N candidate models matches model information corresponding to the first model request message, model information of P candidate models of the M candidate models to the second network element; where N, M, and P are all positive integers, N is greater than or equal to M, and M is greater than or equal to P.

Abstract: Provided are a model obtaining method and a communication device. The method includes: sending, by a first communication device, a first request to a second communication device, where the first request is used to request to obtain information of at least one third communication device, and each third communication device is able to provide model information of at least one model required by the first communication device; receiving, by the first communication device, information of at least one third communication device that is sent by the second communication device; and obtaining, by the first communication device, model information of a plurality of models based on the information of the at least one third communication device, where the plurality of models are used to derive data analytics result information.

Abstract: This application discloses a model information obtaining method and apparatus, a model information sending method and apparatus, a node, and a storage medium. The model information obtaining method in embodiments of this application includes: determining, by a model training function node, a federated learning FL server node; sending, by the model training function node, a first request message to the FL server node, where the first request message is used to trigger the FL server node to perform federated learning to obtain a target model; and receiving, by the model training function node, information about the target model that is sent by the FL server node.

Abstract: Embodiments of this application disclose a communication method and a communication apparatus, to identify application information of first data, and improve an application analytics success rate. The method includes: A first data analytics network element receives first information of an application from a first network element, where the first information includes first application description information and/or an application status that correspond/corresponds to the application, the first application description information includes an application identifier and first IP information that correspond to the application, and the application status includes a foreground state or a background state of the application; the first data analytics network element determines second information based on the first information; and the first data analytics network element sends the second information to a second network element.

Abstract: Embodiments of this application provide a terminal information processing method for determining exception information of a terminal. The method includes: obtaining, by a data analytics network element, data of a first terminal on an application function network element, where the data includes exception information of the first terminal; obtaining, by the data analytics network element, first terminal information of the first terminal on one or more network elements; and determining, by the data analytics network element, exception information of a second terminal based on the exception information of the first terminal and the first terminal information.

Abstract: This application discloses a data acquisition method, a data acquisition apparatus, and a network-side device. The data acquisition method in embodiments of this application includes: transmitting, by a first network element in a first PLMN, a first request to a second network element in a second PLMN, where the first request is used to acquire data generated by a roaming user terminal in the second PLMN and/or a data analytics result generated by the second PLMN for the roaming user terminal, and the roaming user terminal is a user terminal that roams from the first PLMN to the second PLMN or a user terminal that roams from the second PLMN to the first PLMN; and receiving, by the first network element, a first response transmitted by the second network element for the first request.

Abstract: A network element registration method includes sending, by a first network element, a network element registration request to a second network element, where the network element registration request is used to register information of the first network element to the second network element, and the information of the first network element includes model training related capability information of the first network element, where the model training related capability information of the first network element includes at least one of the following: model platform information supported by the first network element; model description method information supported by the first network element; vendor information of the first network element; network element object information for which a model of the first network element can be shared; first indication information; model accuracy information reachable by the first network element; or model training speed information of the first network element.

Abstract: This application discloses a model acquisition method, an information sending method, an information receiving method, an apparatus, and a network element. The model acquisition method of an embodiment of this application includes: receiving, by a first network element, a model storage request message sent by a second network element, where the model storage request message includes model information for at least one model, the model information including at least one of a model file and model file storage information; and storing, by the first network element, the model information for the at least one model.

Abstract: This application discloses a method and an apparatus for determining a candidate member, and a device. The method for determining a candidate member in embodiments of this application includes: receiving, by a first network element, a request message sent by a second network element, where the request message includes filter information; determining, by the first network element based on the filter information, one or more first devices as candidate member(s) able to participate in federated learning; and sending, by the first network element, a response message to the second network element, where the response message includes an identifier of the candidate member, and the filter information includes at least one of the following: a time window, an algorithm type, an accuracy threshold, a wireless access type, a signal quality requirement, a traffic range, member type information, quantity information, area information, and federated learning type information.

Abstract: A URSP rule optimization method includes a first communication device re-evaluates a first URSP rule of a terminal based on target information, to obtain a second URSP rule. The first communication device sends a first message to the terminal. The target information includes a first analytics result, a first evaluation request, or an abnormality indication. The first analytics result is an analytics result and/or a prediction result requested by the first communication device, the first evaluation request is used for the terminal to request a re-evaluation on the first URSP rule, and the abnormality indication indicates that the first URSP rule is executed abnormally. The first message includes the second URSP rule. The second URSP rule is used for the terminal to select and/or establish a PDU session for traffic of an application.