FedOVA: One-vs-All Training Method for Federated Learning with Non-IID Data

Zhu, Y Markos, C Zhao, R Zheng, Y Yu, JJQ

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2021 International Joint Conference on Neural Networks (IJCNN), 2021
Issue Date:: 2021-07-18

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Field	Value	Language
dc.contributor.author	Zhu, Y
dc.contributor.author	Markos, C
dc.contributor.author	Zhao, R
dc.contributor.author	Zheng, Y
dc.contributor.author	Yu, JJQ
dc.date	2021-07-18
dc.date.accessioned	2022-07-02T05:22:06Z
dc.date.available	2022-07-02T05:22:06Z
dc.date.issued	2021-07-18
dc.identifier.citation	2021 International Joint Conference on Neural Networks (IJCNN), 2021
dc.identifier.uri	http://hdl.handle.net/10453/158520
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2021 International Joint Conference on Neural Networks (IJCNN)
dc.relation.ispartof	2021 International Joint Conference on Neural Networks (IJCNN)
dc.relation.isbasedon	10.1109/ijcnn52387.2021.9533409
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	FedOVA: One-vs-All Training Method for Federated Learning with Non-IID Data
dc.type	Conference Proceeding
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	2022-07-02T05:22:04Z
pubs.finish-date	2021-07-22
pubs.publication-status	Published
pubs.start-date	2021-07-18

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