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BASS: A Blockchain-Based Asynchronous SignSGD Architecture for Efficient and Secure Federated Learning

journal contribution
posted on 2024-04-24, 05:33 authored by C Xu, J Ge, Y Deng, Longxiang GaoLongxiang Gao, M Zhang, Y Li, W Zhou, X Zheng
Federated learning (FL) is a distributed framework for machine learning that enables collaborative training of a shared model across data silos while preserving data privacy. However, the FL aggregation server faces a challenge in waiting for a large volume of model parameters from selected nodes before generating a global model, which leads to inefficient communication and aggregation. Although transmitting only the signs of stochastic gradient descent (SignSGD) reduces the transmission load, it decreases model accuracy, and the time waiting for local model collection remains substantial. Moreover, the security of FL is severely compromised by prevalent poisoning, backdoor, and DDoS attacks, causing ineffective and inaccurate model training. To overcome these challenges, this paper proposes a Blockchain-based Asynchronous SignSGD (BASS) architecture for efficient and secure federated learning. By integrating a blockchain-based semi-asynchronous aggregation scheme with sign-based gradient compression, BASS considerably improves communication and aggregation efficiency, while providing resistance against attacks. Besides, a novel node-summarized sign aggregation algorithm is developed for the blockchain leaders to ensure the convergence and accuracy of the global model. An open-source prototype is developed, on top of which extensive experiments are conducted. The results validate the superiority of BASS in terms of efficiency, model accuracy, and security.

History

Journal

IEEE Transactions on Dependable and Secure Computing

Volume

PP

Pagination

1-15

Location

Piscataway, N.J.

ISSN

1545-5971

eISSN

1941-0018

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Issue

99

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

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