To solve the problems caused by using fixed cropping thresholds and noise scales for training in existing differential privacy federated learning algorithms, such as data privacy leakage and low model accuracy, the paper proposed a segmented cropping federated learning algorithm based on differential privacy. Firstly, the clients divided the privacy requirements into high and low privacy demands. For users with high privacy demands, it employed adaptive clipping to dynamically clip the gradients. Conversely, for users with low privacy demands, it adopted proportional clipping. Secondly, the clients adaptively added noise scales based on the size of the clipped threshold. The experimental analysis shows that this algorithm effectively safeguards privacy data, while reducing communication costs compared to ADP-FL and DP-FL algorithms. Additionally, it achieves an improvement in model accuracy by 2.25% and 4.41% compared to ADP-FL and DP-FL respectively.

[2023-11-14] Accepted Paper /doc/2023.09.0402/v1

[2024-05-05] Printed Article /doc/2023.09.0402/v2