Public and Private Blockchain Infusion: A Novel Approach to Federated Learning

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

Public and Private Blockchain Infusion: A Novel Approach to 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, 2024, PP, (99), pp. 1-1
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Wang, M
dc.contributor.author	Zhu, T
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	2024-03-11T05:14:13Z
dc.date.available	2024-03-11T05:14:13Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Internet of Things Journal, 2024, PP, (99), pp. 1-1
dc.identifier.issn	2372-2541
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/176478
dc.description.abstract	Implementing federated learning within the Internet of Everything (IoE) framework poses substantial computational challenges, stemming from extensive client involvement, which can lead to escalated training expenses and diminished convergence rates. While many studies have investigated the combination of federated learning and blockchain networks, the integration of public and private chains to enhance federated learning performance remains largely unexplored. In this study, we introduce an innovative methodology that unifies public and private chains to mitigate clients’ computational demands while preserving data privacy and security, demonstrating compatibility within the IoE milieu and yielding favorable outcomes. To facilitate secure model migration and expedite training without incurring excessive computation costs, we delineate a blockchain-anchored model migration scheme tailored for resource-limited IoT infrastructures, establishing a private chain mechanism to incentivize companies possessing multiple devices or clients to prioritize model training. Employing blockchain technology guarantees trustworthiness in model migration, precluding the disclosure of devices’ confidential data. Overall, our innovative method provides an effective solution that improves the accuracy, privacy, and security of federated learning while reducing clients’ computational burdens within the context of the Internet of Everything (IoE).
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.2024.3360129
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	Public and Private Blockchain Infusion: A Novel Approach to 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/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	2024-03-11T05:14:12Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Implementing federated learning within the Internet of Everything (IoE) framework poses substantial computational challenges, stemming from extensive client involvement, which can lead to escalated training expenses and diminished convergence rates. While many studies have investigated the combination of federated learning and blockchain networks, the integration of public and private chains to enhance federated learning performance remains largely unexplored. In this study, we introduce an innovative methodology that unifies public and private chains to mitigate clients’ computational demands while preserving data privacy and security, demonstrating compatibility within the IoE milieu and yielding favorable outcomes. To facilitate secure model migration and expedite training without incurring excessive computation costs, we delineate a blockchain-anchored model migration scheme tailored for resource-limited IoT infrastructures, establishing a private chain mechanism to incentivize companies possessing multiple devices or clients to prioritize model training. Employing blockchain technology guarantees trustworthiness in model migration, precluding the disclosure of devices’ confidential data. Overall, our innovative method provides an effective solution that improves the accuracy, privacy, and security of federated learning while reducing clients’ computational burdens within the context of the Internet of Everything (IoE).

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http://hdl.handle.net/10453/176478