Distributed machine learning for wireless communication networks: Techniques, architectures, and applications

Hu, S; Chen, X; Ni, W; Hossain, E; Wang, X

Distributed machine learning for wireless communication networks: Techniques, architectures, and applications

Hu, S Chen, X Ni, W

Hossain, E Wang, X

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Communications Surveys and Tutorials, 2021, 23, (3), pp. 1458-1493
Issue Date:: 2021-07-01

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Field	Value	Language
dc.contributor.author	Hu, S
dc.contributor.author	Chen, X
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Hossain, E
dc.contributor.author	Wang, X
dc.date.accessioned	2022-04-15T22:55:31Z
dc.date.available	2022-04-15T22:55:31Z
dc.date.issued	2021-07-01
dc.identifier.citation	IEEE Communications Surveys and Tutorials, 2021, 23, (3), pp. 1458-1493
dc.identifier.issn	1553-877X
dc.identifier.issn	1553-877X
dc.identifier.uri	http://hdl.handle.net/10453/156299
dc.description.abstract	Distributed machine learning (DML) techniques, such as federated learning, partitioned learning, and distributed reinforcement learning, have been increasingly applied to wireless communications. This is due to improved capabilities of terminal devices, explosively growing data volume, congestion in the radio interfaces, and increasing concern of data privacy. The unique features of wireless systems, such as large scale, geographically dispersed deployment, user mobility, and the massive amount of data, give rise to new challenges in the design of DML techniques. There is a clear gap in the existing literature that the DML techniques are yet to be systematically reviewed for their applicability to wireless systems. This survey bridges the gap by providing a contemporary and comprehensive survey of DML techniques with a focus on wireless networks. Specifically, we review the latest applications of DML in power control, spectrum management, user association, and edge cloud computing. The optimality, accuracy, convergence rate, computation cost, and communication overhead of DML are analyzed. We also discuss the potential adversarial attacks faced by DML applications, and describe state-of-the-art countermeasures to preserve privacy and security. Last but not least, we point out a number of key issues yet to be addressed, and collate potentially interesting and challenging topics for future research.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Communications Surveys and Tutorials
dc.relation.isbasedon	10.1109/COMST.2021.3086014
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.title	Distributed machine learning for wireless communication networks: Techniques, architectures, and applications
dc.type	Journal Article
utslib.citation.volume	23
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
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 Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2022-04-15T22:55:29Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	23
utslib.citation.issue	3

Abstract:

Distributed machine learning (DML) techniques, such as federated learning, partitioned learning, and distributed reinforcement learning, have been increasingly applied to wireless communications. This is due to improved capabilities of terminal devices, explosively growing data volume, congestion in the radio interfaces, and increasing concern of data privacy. The unique features of wireless systems, such as large scale, geographically dispersed deployment, user mobility, and the massive amount of data, give rise to new challenges in the design of DML techniques. There is a clear gap in the existing literature that the DML techniques are yet to be systematically reviewed for their applicability to wireless systems. This survey bridges the gap by providing a contemporary and comprehensive survey of DML techniques with a focus on wireless networks. Specifically, we review the latest applications of DML in power control, spectrum management, user association, and edge cloud computing. The optimality, accuracy, convergence rate, computation cost, and communication overhead of DML are analyzed. We also discuss the potential adversarial attacks faced by DML applications, and describe state-of-the-art countermeasures to preserve privacy and security. Last but not least, we point out a number of key issues yet to be addressed, and collate potentially interesting and challenging topics for future research.

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http://hdl.handle.net/10453/156299