Bayesian Federated Learning: A Survey

Cao, L; Chen, H; Fan, X; Gama, J; Ong, YS; Kumar, V

Bayesian Federated Learning: A Survey

Cao, L

Chen, H Fan, X Gama, J Ong, YS Kumar, V

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Publication Type:: Conference Proceeding
Citation:: IJCAI International Joint Conference on Artificial Intelligence, 2023, 2023-August, pp. 7233-7242
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429
dc.contributor.author	Chen, H
dc.contributor.author	Fan, X
dc.contributor.author	Gama, J
dc.contributor.author	Ong, YS
dc.contributor.author	Kumar, V
dc.date.accessioned	2024-05-21T05:39:59Z
dc.date.available	2024-05-21T05:39:59Z
dc.date.issued	2023-01-01
dc.identifier.citation	IJCAI International Joint Conference on Artificial Intelligence, 2023, 2023-August, pp. 7233-7242
dc.identifier.isbn	9781956792034
dc.identifier.issn	1045-0823
dc.identifier.uri	http://hdl.handle.net/10453/179077
dc.description.abstract	Federated learning (FL) demonstrates its advantages in integrating distributed infrastructure, communication, computing and learning in a privacy-preserving manner. However, the robustness and capabilities of existing FL methods are challenged by limited and dynamic data and conditions, complexities including heterogeneities and uncertainties, and analytical explainability. Bayesian federated learning (BFL) has emerged as a promising approach to address these issues. This survey presents a critical overview of BFL, including its basic concepts, its relations to Bayesian learning in the context of FL, and a taxonomy of BFL from both Bayesian and federated perspectives. We categorize and discuss client- and server-side and FL-based BFL methods and their pros and cons. The limitations of the existing BFL methods and the future directions of BFL research further address the intricate requirements of real-life FL applications.
dc.language	en
dc.relation.ispartof	IJCAI International Joint Conference on Artificial Intelligence
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Bayesian Federated Learning: A Survey
dc.type	Conference Proceeding
utslib.citation.volume	2023-August
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 - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access	*
dc.date.updated	2024-05-21T05:39:57Z
pubs.publication-status	Published
pubs.volume	2023-August

Abstract:

Federated learning (FL) demonstrates its advantages in integrating distributed infrastructure, communication, computing and learning in a privacy-preserving manner. However, the robustness and capabilities of existing FL methods are challenged by limited and dynamic data and conditions, complexities including heterogeneities and uncertainties, and analytical explainability. Bayesian federated learning (BFL) has emerged as a promising approach to address these issues. This survey presents a critical overview of BFL, including its basic concepts, its relations to Bayesian learning in the context of FL, and a taxonomy of BFL from both Bayesian and federated perspectives. We categorize and discuss client- and server-side and FL-based BFL methods and their pros and cons. The limitations of the existing BFL methods and the future directions of BFL research further address the intricate requirements of real-life FL applications.

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http://hdl.handle.net/10453/179077