Restrictively Self-sampled and Compressed Local Differential Privacy in Federated Learning

Abstract

As a popular machine learning framework, federated learning (FL) enables clients to conduct cooperative training without sharing data, thus having higher security than conventional machine learning. However, by sharing parameters in the federated learning process, the attacker can still obtain private information from the sensitive data of participants by reverse parsing. Recently, local differential privacy (LDP) has worked well in preserving privacy for federated learning. However, it faces the inherent problem of balancing privacy, model performance, and algorithm efficiency. In this paper, we propose a novel local differential privacy method in federated learning (SLDP-FL), which achieves the privacy amplification effect by the client self-sampling and provides compressed and private parameters in each iteration by a compressed LDP mechanism. Thereby, it improves the model performance as well as efficiency observably.Moreover, we theoretically analyze the relationship between the model accuracy and client self-sampling probability. A restrictive client self-sampling technology is proposed, which eliminates the randomness of self-sampling probability settings in existing studies and improves the utilization of the federated system. Comprehensive experiments on MNIST and Fashion-MNIST datasets show that the SLDP-FL optimizes the existing federated learning framework through compression mechanism and self-sampling technique with restrictive probability since it is superior to the current algorithms' accuracy and convergence and communication efficiency.