Abstract: Federated learning is an emerging privacy-preserving distributed machine learning framework,whose core feature is the ability to implement distributed machine learning without access to the client’s raw data.The client uses local data for model training and then uploads the model parameters to the server for aggregation,thus ensuring that the client data is always protected.In this process,there are problems of high communication costs due to frequent parameter transfers and non-independent homogeneous heterogeneous data owned by each client,both of which severely limit the application of federated learning.To address these problems,FedPSG,a federated learning method based on particle swarm optimization for data heterogeneity,is proposed to reduce the communication cost by changing the form of data transferred from the client to the server from model para-meters to model scores,so that only a small number of clients need to upload model parameters to the server in each training round.Meanwhile,a model retraining strategy is proposed to use the server data to train the global model for a second iteration,further improving the model performance by mitigating the impact of data heterogeneity issues on federated learning.Simulating different data heterogeneous environments,experiments are conducted on MNIST,FashionMNIST and CIFAR-10 datasets.The results show that FedPSG can effectively improve the accuracy of the model in different data heterogeneous environments,and verify that the model retraining strategy can effectively solve the client-side data heterogeneity problem.

Key words: Federated learning, Particle swarm algorithm, Communication cost, Data heterogeneity, Privacy protection