Kalman Filter-Based Differential Privacy Federated Learning Method
Xiaohui Yang, Zijian Dong · Applied Sciences · 2022
The data privacy leakage problem of federated learning has attracted widespread attention. Using differential privacy can protect the data privacy of each node in the federated learning, but adding noise to the model parameters will reduce the accuracy and convergence efficiency of the model. A Kalman Filter-based Differential Privacy Federated Learning Method (KDP-FL) has been proposed to solve this problem, which reduces the impact of the noise added on the model by Kalman filtering. Furthermore, the effectiveness of the proposed method is verified in the case of both Non-IID and IID data distributions. The experiments show that the accuracy of the proposed method is improved by 0.3–4.5% compared to differential privacy federated learning.