Kalman Filter-Based Differential Privacy Federated Learning Method

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.

Automated summary

This paper investigates the issue of data privacy leakage in federated learning and proposes a Kalman Filter-based Differential Privacy Federated Learning Method. Experimental results demonstrate that the proposed method outperforms traditional differential privacy federated learning in terms of accuracy.