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Federated Learning for Energy-balanced Client Selection in Mobile Edge Computing

2021-07-02Journal article Open access
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dc.contributor.authorZheng, Jingjing
dc.contributor.authorLi, Kai
dc.contributor.authorTovar, Eduardo
dc.contributor.authorGuizani, Mohsen
dc.date.accessioned2021-08-30T10:48:48Z
dc.date.available2021-08-30T10:48:48Z
dc.date.issued2021-07-02
dc.description.abstractMobile edge computing (MEC) has been considered as a promising technology to provide seamless integration of multiple application services. Federated learning (FL) is carried out at edge clients in MEC for privacy-preserving training of data processing models. Despite that the edge clients with small data payloads consume less energy on FL training, the small data payload gives rise to a low learning accuracy due to insufficient input to the FL training. Inadequate selection of the edge clients can result in a large energy consumption at the edge clients, or a low learning accuracy of the FL training. In this paper, a new FL-based client selection optimization is proposed to balance the trade-off between energy consumption of the edge clients and the learning accuracy of FL. We first show that this optimization problem is NP-complete. Next, we propose a FL-based energy-accuracy balancing heuristic algorithm to approximate the optimal client selection in polynomial time. The numerical results show the advantage of our proposed algorithm.pt_PT
dc.description.sponsorshipThis work was partially supported by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit (UIDP/UIDB/04234/2020); also by the Operational Competitiveness Programme and Internationalization (COMPETE 2020) through the European Regional Development Fund (ERDF) and by national funds through the FCT, within project POCI-01-0145-FEDER-029074 (ARNET).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/IWCMC51323.2021.9498853pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/18259
dc.language.isoengpt_PT
dc.publisherIEEEpt_PT
dc.relationUIDP/UIDB/04234/2020pt_PT
dc.relationPOCI-01-0145-FEDER-029074pt_PT
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9498853pt_PT
dc.subjectClient selectionpt_PT
dc.subjectMobile edge computingpt_PT
dc.subjectFederated learningpt_PT
dc.subjectHeuristic algorithmpt_PT
dc.titleFederated Learning for Energy-balanced Client Selection in Mobile Edge Computingpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.conferencePlaceHarbin City, Chinapt_PT
oaire.citation.title2021 International Wireless Communications and Mobile Computing (IWCMC)pt_PT
oaire.citation.volume210305pt_PT
person.familyNameLi
person.familyNameTovar
person.givenNameKai
person.givenNameEduardo
person.identifier.ciencia-idEE10-B822-16ED
person.identifier.ciencia-id6017-8881-11E8
person.identifier.orcid0000-0002-0517-2392
person.identifier.orcid0000-0001-8979-3876
person.identifier.scopus-author-id7006312557
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication21f3fb85-19c2-4c89-afcd-3acb27cedc5e
relation.isAuthorOfPublication80b63d8a-2e6d-484e-af3c-55849d0cb65e
relation.isAuthorOfPublication.latestForDiscovery80b63d8a-2e6d-484e-af3c-55849d0cb65e

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