Blockchain-Based Gradient Inversion and Poisoning Defense for Federated Learning

Wang, M; Zhu, T; Zuo, X; Ye, D; Yu, S; Zhou, W

Blockchain-Based Gradient Inversion and Poisoning Defense for Federated Learning

Wang, M Zhu, T

Zuo, X Ye, D

Yu, S

Zhou, W

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2023, PP, (99), pp. 1-1
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Wang, M
dc.contributor.author	Zhu, T https://orcid.org/0000-0003-3411-7947
dc.contributor.author	Zuo, X
dc.contributor.author	Ye, D https://orcid.org/0000-0002-7561-0992
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.contributor.author	Zhou, W
dc.date.accessioned	2024-03-27T05:26:27Z
dc.date.available	2024-03-27T05:26:27Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Internet of Things Journal, 2023, PP, (99), pp. 1-1
dc.identifier.issn	2372-2541
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/177270
dc.description.abstract	Federated learning has emerged as a promising privacy-preserving machine learning technology, enabling multiple clients to collaboratively train a global model without sharing raw data. With the increasing adoption of federated learning in Internet of Things (IoT) scenarios, concerns about security and privacy have become critical. In particular, gradient inversion attacks and poisoning attacks pose significant threats to the integrity and effectiveness of the global model. In response, we propose a comprehensive blockchain-based defense mechanism that effectively protects federated learning systems from such attacks. We develop a novel combination of techniques, including public blockchain level protection and private blockchain level protection, which work in tandem to prevent attackers from reconstructing figures using the obtained gradients. This unique combination of methods provides a robust defense against gradient inversion attacks in federated learning IoT scenarios. We conduct extensive experiments to validate the effectiveness of our proposed approach against gradient inversion and poisoning attacks. Our results demonstrate improved accuracy and stable convergence of training loss under poisoning attacks, indicating that our method can be applied to a wide range of federated learning IoT scenarios, enhancing both the security and privacy of distributed machine learning systems.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	http://purl.org/au-research/grants/arc/LP220200808
dc.relation.ispartof	IEEE Internet of Things Journal
dc.relation.isbasedon	10.1109/JIOT.2023.3347899
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Blockchain-Based Gradient Inversion and Poisoning Defense for Federated Learning
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0805 Distributed Computing
utslib.for	1005 Communications Technologies
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/Strength - CCSP - Centre for Cyber Security and Privacy
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-27T05:26:25Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Federated learning has emerged as a promising privacy-preserving machine learning technology, enabling multiple clients to collaboratively train a global model without sharing raw data. With the increasing adoption of federated learning in Internet of Things (IoT) scenarios, concerns about security and privacy have become critical. In particular, gradient inversion attacks and poisoning attacks pose significant threats to the integrity and effectiveness of the global model. In response, we propose a comprehensive blockchain-based defense mechanism that effectively protects federated learning systems from such attacks. We develop a novel combination of techniques, including public blockchain level protection and private blockchain level protection, which work in tandem to prevent attackers from reconstructing figures using the obtained gradients. This unique combination of methods provides a robust defense against gradient inversion attacks in federated learning IoT scenarios. We conduct extensive experiments to validate the effectiveness of our proposed approach against gradient inversion and poisoning attacks. Our results demonstrate improved accuracy and stable convergence of training loss under poisoning attacks, indicating that our method can be applied to a wide range of federated learning IoT scenarios, enhancing both the security and privacy of distributed machine learning systems.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/177270