HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks

Nguyen, M-D Lee, S-M Pham, Q-V Hoang, DT Nguyen, DN Hwang, W-J

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Field	Value	Language
dc.contributor.author	Nguyen, M-D
dc.contributor.author	Lee, S-M
dc.contributor.author	Pham, Q-V
dc.contributor.author	Hoang, DT
dc.contributor.author	Nguyen, DN
dc.contributor.author	Hwang, W-J
dc.date.accessioned	2023-03-13T23:14:34Z
dc.date.available	2023-03-13T23:14:34Z
dc.date.issued	2022-04-14
dc.identifier.citation	arXiv, 2022
dc.identifier.uri	http://hdl.handle.net/10453/167245
dc.language	en
dc.relation.ispartof	arXiv
dc.relation.isbasedon	10.48550/arxiv.2204.06760
dc.rights	info:eu-repo/semantics/openAccess
dc.title	HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks
dc.type	Preprint
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 - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-13T23:14:31Z

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