"Privacy-preserving Byzantine-robust federated learning via blockchain " by Yinbin MIAO, Ziteng LIU et al.
 

Privacy-preserving Byzantine-robust federated learning via blockchain systems

Publication Type

Journal Article

Publication Date

8-2022

Abstract

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.

Keywords

Federated learning, poisoning attacks, fully homomorphic encryption, blockchain

Discipline

Information Security

Research Areas

Cybersecurity

Publication

IEEE Transactions on Information Forensics and Security

Volume

17

First Page

2848

Last Page

2861

ISSN

1556-6013

Identifier

10.1109/TIFS.2022.3196274

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/TIFS.2022.3196274

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