Abstract: Federated learning trains a global model by exchanging model parameters rather than data,with the goal of achieving privacy protection.However,a large number of studies have shown that attackers can infer the original training data through intercepted gradients,leading to privacy leakage on clients.In addition,the different sampling methods of different clients can lead to the phenomenon of non independent and identically distributed collected data,which can affect the overall training performance of the model.To cope with gradient inversion attacks,the data distillation method is introduced into the federated learning framework,while combining data augmentation methods to enhance the availability of synthesized data.In addition,to address the issue of data heterogeneity in medical data from different institutions,a batch normalization layer is introduced into the client to alleviate client drift and improve the overall performance of the model.Experimental results indicate that while achieving similar performance to other federated learning paradigms,the federated learning method combined with data distillation also enhances the protection of medical data privacy.

Key words: Federated learning, Privacy protection, Data distillation, Image classification, Data heterogeneity