Federated Learning Privacy-preserving Method Based on Bregman Optimization

Abstract

Federated learning has received a lot of attention for its ability to solve the data silo problem, but it is also limited by the problem of data heterogeneity and privacy. Non-Independent Identical Distribution (Non-I.I.D) data leads to performance degradation of federation models, and privacy problem have been studied as a hot topic in the field of federated learning. However, current research rarely considers non-I.I.D data and privacy simultaneously. In this paper, we propose a federated learning scheme based on Bregman and differential privacy (FLBDP). Our approach adopts Bregman distance for personalized model training, which aims to control the difference between local model and global model in a limited range, the FLBDP can reduce the model difference to improve the model performance by Bregman optimization. In addition, we use a Gaussian mechanism to perturb the personalized model and update the local model by the perturbed personalized model, which enables the model parameters to satisfy differential privacy in the uplink channel to enhance user privacy protection.