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: 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: 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: 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 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.

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: A network element registration method includes sending, by a first network element, a registration request to a second network element, where the registration request is used to request registration of federated learning capability information of the first network element with the second network element. The federated learning capability information of the first network element includes at least one of the following type information of federated learning training supported by the first network element, time information of federated learning training supported by the first network element, or metadata information possessed by the first network element.

Abstract: An information transmission method and a network element. The information transmission method of embodiments of this application includes: A first core network element receives a location service request from a second core network element. The location service request is used to request location information of at least one user equipment, the location service request includes location granularity indication information, and the location granularity indication information is used to indicate at least one of location granularities of multiple gradients. The first core network element determines location accuracy and a quality of service QoS class based on the location service request. The first core network element sends a user equipment location service request to a first network element. The first core network element receives the user equipment location information sent by the first network element.

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 user equipment information prediction method and apparatus and a network element. The method in embodiments of this application includes: obtaining, by a first network element, user equipment location data of a first time period using a location service LCS architecture; and inputting, by the first network element, the user equipment location data of the first time period into a traffic model or a supervised learning model, and obtaining user equipment prediction information of a second time period output by the traffic model or the supervised learning model.

Abstract: A device determining method includes sending, by a first communication device, a first request message to a second communication device, where the first request message is used for requesting to obtain network performance analytics information; and receiving, by the first communication device, the network performance analytics information from the second communication device, where the network performance analytics information includes network performance analytics information corresponding to M candidate devices. Network performance analytics information corresponding to one candidate device includes at least one of the following information: information about a time at which the one candidate device is able to participate in federated learning; information about a time period in which the one candidate device has network coverage; or network signal quality information of the one candidate device.

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: Provided are a method and an apparatus for determining a user plane function network element. The method includes: transmitting, by a user plane function network element, a network element registration request message to a first network element, where the network element registration request message is used to register network element information of the user plane function network element to the first network element; and obtaining, by the user plane function network element, a network element registration response message transmitted by the first network element.

Abstract: This application discloses a data collection method and a communication device. The data collection method includes: A first communication device sends a first request to a target communication device, where the first request is used to request to obtain target data collected by a third communication device corresponding to a target terminal, and the target data is used by the first communication device to perform target processing; and the first communication device obtains the target data collected by the third communication device.

Abstract: A data processing method and an apparatus relate to the communication field. The data processing method includes: A first network data analytics network element determines identification information of one or more submodels related to a target model. The first network data analytics network element sends the identification information of the submodel to a network element corresponding to the identification information of the submodel. The first network data analytics network element receives, from the network element, a value that reflects impact of the submodel on the target model, and performs training, to obtain the target model. The data processing method and apparatus provide functions such as accurate data collection, data analysis, and data training.

Abstract: A model accuracy determining method includes performing, by a first network element, inference on a task based on a first model; determining, by the first network element, first accuracy corresponding to the first model. The first accuracy is used to indicate accuracy of the first model on an inference result of the task; and in a case that the first accuracy reaches a preset condition, sending, by the first network element, first information to a second network element, where the first information is used to indicate that accuracy of the first model does not meet an accuracy requirement or decreases. The second network element is a network element that provides the first model.