Enhancing privacy preservation and trustworthiness for decentralized federated learning

Abstract

Decentralized federated learning (DFL) is an emerging privacy-preserving machine learning framework, where multiple data owners cooperate to train a global model without any aggregation server. In addition to protecting data privacy, it also avoids a single point of failure, and reduces the communication traffic congestion due to the central server. However, there are still many inherent privacy and security issues, e.g., training samples could be revealed by inferring gradients and malicious participants tend not to execute federated learning tasks as intended. Even worse, existing works rarely simultaneously consider a fundamental issue that data owners may join in and drop out of a DFL task at any time. In this paper, we propose PTDFL, a privacy-enhanced and trustworthy decentralized federated learning scheme. Specifically, we firstly design an efficient gradient encryption algorithm to protect data privacy, and then devise a succinct proof without trapdoors to ensure the correctness of the gradients. Meanwhile, we design a novel local aggregation strategy without trusted third party to ensure that the aggregated result is trustworthy. Moreover, our PTDFL is also supportive of data owners joining in and dropping out during the whole DFL task. Finally, we provide privacy and security analysis, implement a prototype of the PTDFL, and conduct extensive experiments on a real dataset. The results show that our PTDFL achieves more efficiency than prior works.