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dc.contributor.authorChen, Zen_US
dc.contributor.authorYi, Wen_US
dc.contributor.authorLiu, Yen_US
dc.contributor.authorNallanathan, Aen_US
dc.date.accessioned2024-07-12T13:30:31Z
dc.date.issued2023en_US
dc.identifier.issn0090-6778en_US
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/98091
dc.format.extent3368 - 3386en_US
dc.relation.ispartofIEEE TRANSACTIONS ON COMMUNICATIONSen_US
dc.rights© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subjectDevice schedulingen_US
dc.subjectLyapunov optimizationen_US
dc.subjectpersonalized federated learningen_US
dc.subjectresource allocationen_US
dc.titleKnowledge-Aided Federated Learning for Energy-Limited Wireless Networksen_US
dc.typeArticle
dc.identifier.doi10.1109/TCOMM.2023.3261383en_US
pubs.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:001013660800015&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=612ae0d773dcbdba3046f6df545e9f6aen_US
pubs.issue6en_US
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US
pubs.volume71en_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US
rioxxterms.funder.projectb215eee3-195d-4c4f-a85d-169a4331c138en_US


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