With the explosive growth of personal data,the federated learning based on differential privacy can be used to solve the problem of data islands and preserve user data privacy.Participants share the parameters with noise to the central server for aggregation by training local data,and realize distributed machine learning training.However,there are two defects in this model:on the one hand,the data information in the process of parameters broadcasting by the central server is still compromised,with the risk of user privacy leakage;on the other hand,adding too much noise to parameters will reduce the quality of parameter aggregation and affect the model accuracy of federated learning.In order to solve the above problems,a bidirectional adaptive differential privacy federated learning scheme(Federated Learning Approach with Bidirectional Adaptive Differential Privacy,FedBADP)is proposed,which can adaptively add noise to the gradients transmitted by participants and central servers,and keep data security without affecting the model accuracy.Meanwhile,considering the performance limitations of the participants hardware devices,this model samples their gradients to reduce the communication overhead,and uses the RMSprop to accelerate the convergence of the model on the participants and central server to improve the accuracy of the model.Experiments show that our novel model can enhance the user privacy preserving while maintaining a good accuracy.
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