Abstract: Embodiments of this application provide a model training method. The system includes: A client is configured to train a first model based on unlabeled data, and is further configured to send a parameter of a first subnet in the first model to a server; the server is configured to train a second model based on the parameter of the first subnet and labeled data, to update a parameter of the second model, and the server is further configured to send an updated parameter of the first subnet and an updated parameter of a third subnet to the client; and the client is further configured to obtain a target model based on the parameter of the first subnet and the parameter of the third subnet that are from the server.

Abstract: This application provides a federated learning method and apparatus. The method includes: A first server receives a request message sent by at least one first client. The first server sends a training configuration parameter and a global model to the at least one first client. The first server receives first model update parameters separately fed back by the at least one first client. The first server aggregates the first model update parameters, to obtain first aggregation information in a current round of iteration. The first server obtains second aggregation information sent by the second server. The first server updates, based on the first aggregation information and the second aggregation information, the global model stored on the first server.