LSFL: A Lightweight and Secure Federated Learning Scheme for Edge Computing

Zhang, Z; Wu, L; Ma, C; Li, J; Wang, J; Wang, Q; Yu, S

LSFL: A Lightweight and Secure Federated Learning Scheme for Edge Computing

Zhang, Z Wu, L Ma, C Li, J Wang, J Wang, Q Yu, S

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Information Forensics and Security, 2023, 18, pp. 365-379
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Zhang, Z
dc.contributor.author	Wu, L
dc.contributor.author	Ma, C
dc.contributor.author	Li, J
dc.contributor.author	Wang, J
dc.contributor.author	Wang, Q
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2024-03-12T00:00:10Z
dc.date.available	2024-03-12T00:00:10Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Transactions on Information Forensics and Security, 2023, 18, pp. 365-379
dc.identifier.issn	1556-6013
dc.identifier.issn	1556-6021
dc.identifier.uri	http://hdl.handle.net/10453/176506
dc.description.abstract	Nowadays, many edge computing service providers expect to leverage the computational power and data of edge nodes to improve their models without transmitting data. Federated learning facilitates collaborative training of global models among distributed edge nodes without sharing their training data. Unfortunately, existing privacy-preserving federated learning applied to this scenario still faces three challenges: 1) It typically employs complex cryptographic algorithms, which results in excessive training overhead; 2) It cannot guarantee Byzantine robustness while preserving data privacy; and 3) Edge nodes have limited computing power and may drop out frequently. As a result, the privacy-preserving federated learning cannot be effectively applied to edge computing scenarios. Therefore, we propose a lightweight and secure federated learning scheme LSFL, which combines the features of privacy-preserving and Byzantine-Robustness. Specifically, we design the Lightweight Two-Server Secure Aggregation protocol, which utilizes two servers to enable secure Byzantine robustness and model aggregation. This scheme protects data privacy and prevents Byzantine nodes from influencing model aggregation. We implement and evaluate LSFL in a LAN environment, and the experiment results show that LSFL meets fidelity, security, and efficiency design goals, and maintains model accuracy compared to the popular FedAvg scheme.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Information Forensics and Security
dc.relation.isbasedon	10.1109/TIFS.2022.3221899
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Strategic, Defence & Security Studies
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	LSFL: A Lightweight and Secure Federated Learning Scheme for Edge Computing
dc.type	Journal Article
utslib.citation.volume	18
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
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	*
pubs.consider-herdc	false
dc.date.updated	2024-03-12T00:00:09Z
pubs.publication-status	Published
pubs.volume	18

Abstract:

Nowadays, many edge computing service providers expect to leverage the computational power and data of edge nodes to improve their models without transmitting data. Federated learning facilitates collaborative training of global models among distributed edge nodes without sharing their training data. Unfortunately, existing privacy-preserving federated learning applied to this scenario still faces three challenges: 1) It typically employs complex cryptographic algorithms, which results in excessive training overhead; 2) It cannot guarantee Byzantine robustness while preserving data privacy; and 3) Edge nodes have limited computing power and may drop out frequently. As a result, the privacy-preserving federated learning cannot be effectively applied to edge computing scenarios. Therefore, we propose a lightweight and secure federated learning scheme LSFL, which combines the features of privacy-preserving and Byzantine-Robustness. Specifically, we design the Lightweight Two-Server Secure Aggregation protocol, which utilizes two servers to enable secure Byzantine robustness and model aggregation. This scheme protects data privacy and prevents Byzantine nodes from influencing model aggregation. We implement and evaluate LSFL in a LAN environment, and the experiment results show that LSFL meets fidelity, security, and efficiency design goals, and maintains model accuracy compared to the popular FedAvg scheme.

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http://hdl.handle.net/10453/176506