Privacy-Preserving Byzantine-Robust Federated Learning via Blockchain Systems

• Published in: IEEE transactions on information forensics and security Vol. 17; pp. 2848 - 2861
• Main Authors: Miao, Yinbin, Liu, Ziteng, Li, Hongwei, Choo, Kim-Kwang Raymond, Deng, Robert H.
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Blockchain; Blockchains; Clients; Collaborative work; Computational modeling; Cryptography; Datasets; Federated learning; fully homomorphic encryption; Machine learning; poisoning attacks; Privacy; Resists; Robustness; Servers; Training
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2022.3196274

Federated learning enables clients to train a machine learning model jointly without sharing their local data. However, due to the centrality of federated learning framework and the untrustworthiness of clients, traditional federated learning solutions are vulnerable to poisoning attacks from malicious clients and servers. In this paper, we aim to mitigate the impact of the central server and malicious clients by designing a Privacy-preserving Byzantine-robust Federated Learning (PBFL) scheme based on blockchain. Specifically, we use cosine similarity to judge the malicious gradients uploaded by malicious clients. Then, we adopt fully homomorphic encryption to provide secure aggregation. Finally, we use blockchain system to facilitate transparent processes and implementation of regulations. Our formal analysis proves that our scheme achieves convergence and provides privacy protection. Our extensive experiments on different datasets demonstrate that our scheme is robust and efficient. Even if the root dataset is small, our scheme can achieve the same efficiency as FedSGD.