Hybrid NOMA for Latency Minimization in Wireless Federated Learning for 6G Networks

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dc.contributor.authorKavitha, P.
dc.contributor.authorKavitha, K.
dc.coverage.issue4cs
dc.coverage.volume32cs
dc.date.accessioned2024-01-09T14:20:51Z
dc.date.available2024-01-09T14:20:51Z
dc.date.issued2023-12cs
dc.description.abstractWireless Federated Learning (WFL) is an innovative machine learning paradigm enabling distributed devices to collaboratively learn without sharing raw data. WFL is particularly useful for mobile devices that generate massive amounts of data but have limited resources for training complex models. This paper highlights the significance of reducing delay for efficient WFL implementation through advanced multiple access protocols and joint optimization of communication and computing resources. We propose optimizing the WFL Compute-then-Transmit (CT) protocol using hybrid Non-Orthogonal Multiple Access (H-NOMA). To minimize and optimize latency for the transmission of local training data, we use the Successive Convex Optimization (SCA) method, which efficiently reduces the complexity of non-convex algorithms. Finally, the numerical results verify the effectiveness of H-NOMA in terms of delay reduction, compared to the benchmark that is based on Non-Orthogonal Multiple Acces (NOMA).en
dc.formattextcs
dc.format.extent594-602cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2023 vol. 32, č. 4, s. 594-602. ISSN 1210-2512cs
dc.identifier.doi10.13164/re.2023.0594en
dc.identifier.issn1210-2512
dc.identifier.urihttps://hdl.handle.net/11012/244203
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttps://www.radioeng.cz/fulltexts/2023/23_04_0594_0602.pdfcs
dc.rightsCreative Commons Attribution 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectWFLen
dc.subjectNOMAen
dc.subjectSCAen
dc.subjectlatencyen
dc.subjectCompute-then-Transmit (CT)en
dc.titleHybrid NOMA for Latency Minimization in Wireless Federated Learning for 6G Networksen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
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