Privacy and Fairness in Federated Learning: On the Perspective of Tradeoff

Chen, H; Zhu, T; Zhang, T; Zhou, W; Yu, PS

Privacy and Fairness in Federated Learning: On the Perspective of Tradeoff

Chen, H Zhu, T

Zhang, T Zhou, W Yu, PS

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Journal Article
Citation:: ACM Computing Surveys, 2023, 56, (2), pp. 1-37
Issue Date:: 2023-09-15

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Field	Value	Language
dc.contributor.author	Chen, H
dc.contributor.author	Zhu, T https://orcid.org/0000-0003-3411-7947
dc.contributor.author	Zhang, T
dc.contributor.author	Zhou, W
dc.contributor.author	Yu, PS
dc.date.accessioned	2024-03-13T22:39:33Z
dc.date.available	2024-03-13T22:39:33Z
dc.date.issued	2023-09-15
dc.identifier.citation	ACM Computing Surveys, 2023, 56, (2), pp. 1-37
dc.identifier.issn	0360-0300
dc.identifier.issn	1557-7341
dc.identifier.uri	http://hdl.handle.net/10453/176650
dc.description.abstract	Federated learning (FL) has been a hot topic in recent years. Ever since it was introduced, researchers have endeavored to devise FL systems that protect privacy or ensure fair results, with most research focusing on one or the other. As two crucial ethical notions, the interactions between privacy and fairness are comparatively less studied. However, since privacy and fairness compete, considering each in isolation will inevitably come at the cost of the other. To provide a broad view of these two critical topics, we presented a detailed literature review of privacy and fairness issues, highlighting unique challenges posed by FL and solutions in federated settings. We further systematically surveyed different interactions between privacy and fairness, trying to reveal how privacy and fairness could affect each other and point out new research directions in fair and private FL.
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation	http://purl.org/au-research/grants/arc/DP200100946
dc.relation	http://purl.org/au-research/grants/arc/DP230100246
dc.relation.ispartof	ACM Computing Surveys
dc.relation.isbasedon	10.1145/3606017
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	08 Information and Computing Sciences
dc.subject.classification	Information Systems
dc.subject.classification	46 Information and computing sciences
dc.title	Privacy and Fairness in Federated Learning: On the Perspective of Tradeoff
dc.type	Journal Article
utslib.citation.volume	56
utslib.for	08 Information and Computing Sciences
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	in_progress	*
dc.date.updated	2024-03-13T22:39:32Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	56
utslib.citation.issue	2

Abstract:

Federated learning (FL) has been a hot topic in recent years. Ever since it was introduced, researchers have endeavored to devise FL systems that protect privacy or ensure fair results, with most research focusing on one or the other. As two crucial ethical notions, the interactions between privacy and fairness are comparatively less studied. However, since privacy and fairness compete, considering each in isolation will inevitably come at the cost of the other. To provide a broad view of these two critical topics, we presented a detailed literature review of privacy and fairness issues, highlighting unique challenges posed by FL and solutions in federated settings. We further systematically surveyed different interactions between privacy and fairness, trying to reveal how privacy and fairness could affect each other and point out new research directions in fair and private FL.

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http://hdl.handle.net/10453/176650