Abstract: A federated learning method includes: obtaining a target federated sub-model of a target participating node, the target federated sub-model being obtained upon dividing up a federated model, the federated model comprising at least three federated sub-models, and the target federated sub-model comprising a model parameter and a target feature of the target participating node; obtaining a current network delay and preset instances of optimization of the target feature; determining current instances of optimization on the basis of the difference between the current network delay and a preset network delay corresponding to the preset instances of optimization; performing local optimization on the target feature according to the current instances of optimization; and performing encrypted interaction of an optimization result of the target feature with other participating nodes, so as to optimize a model parameter and determine a target model parameter of the target federated sub-model.