Blockchain-Based Decentralized and Lightweight Anonymous Authentication for Federated Learning

Authors

Mochan Fan, University of Electronic Science and Technology of China
Zhipeng Zhang, University of Electronic Science and Technology of China
Zonghang Li, University of Electronic Science and Technology of China
Gang Sun, University of Electronic Science and Technology of China
Hongfang Yu, University of Electronic Science and Technology of China
Mohsen Guizani, Mohamed Bin Zayed University of Artificial IntelligenceFollow

Document Type

Article

Publication Title

IEEE Transactions on Vehicular Technology

Abstract

Federated learning (FL) is a promising technology for achieving privacy-preserving edge intelligence and has attracted extensive attention from industry and academia. However, in the FL training process, the server directly aggregates local models from mobile devices, which poses serious privacy and security threats. The identity authentication mechanism can provide FL with local model integrity and source authentication. However, the existing schemes are centralized, and most of them are computationally expensive, resulting in limited performance. To address these issues, this paper proposes a decentralized and lightweight anonymous FL identity authentication scheme, namely DAFL. In our scheme, we first design a decentralized and simplified storage FL authentication framework by combining the directed acyclic graph (DAG) blockchain and accumulator. Then, we propose a lightweight digital signature algorithm that supports batch verification for authentication. Finally, nodes interact through pseudonyms to achieve anonymous communication, and the trusted authority (TA) can track and recover the real identities of nodes when malicious behavior occurs. We theoretically prove the security of the proposed DAFL. The extensive experiments demonstrate that DAFL achieves lower authentication overhead and better convergence performance compared to existing authentication schemes and vanilla FL systems.

First Page

1

Last Page

13

DOI

10.1109/TVT.2023.3265366

Publication Date

4-7-2023

Keywords

Authentication, authentication, batch verification, Blockchain, Blockchains, federated learning, Heuristic algorithms, Public key, Security, Servers, Sun

Comments

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OA version (pathway a) Accepted version

No embargo

When accepted for publication, set statement to accompany deposit (see policy)

Must link to publisher version with DOI

Publisher copyright and source must be acknowledged

Recommended Citation

M. Fan, Z. Zhang, Z. Li, G. Sun, H. Yu and M. Guizani, "Blockchain-Based Decentralized and Lightweight Anonymous Authentication for Federated Learning," in IEEE Transactions on Vehicular Technology, April 2023, doi: 10.1109/TVT.2023.3265366.