Federated Learning for Physical Layer Design

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dc.authoridElbir, Ahmet M./0000-0003-4060-3781
dc.authoridChatzinotas, Symeon/0000-0001-5122-0001
dc.authorwosidElbir, Ahmet M./X-3731-2019
dc.authorwosidChatzinotas, Symeon/D-4191-2015
dc.contributor.authorElbir, Ahmet M.
dc.contributor.authorPapazafeiropoulos, Anastasios K.
dc.contributor.authorChatzinotas, Symeon
dc.date.accessioned2023-07-26T11:55:08Z
dc.date.available2023-07-26T11:55:08Z
dc.date.issued2021
dc.departmentDÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractModel-free techniques, such as machine learning (ML), have recently attracted much interest toward the physical layer design (e.g., symbol detection, channel estimation, and beamforming). Most of these ML techniques employ centralized learning (CLK) schemes and assume the availability of datasets at a parameter server (PS), demanding the transmission of data from edge devices, such as mobile phones, to the PS. Exploiting the data generated at the edge, federated learning (FL) has been proposed recently as a distributed learning scheme, in which each device computes the model parameters and sends them to the PS for model aggregation, while the datasets are kept intact at the edge. Thus, FL is more communication-efficient and privacy-preserving than CL and applicable to the wireless communication scenarios, wherein the data are generated at the edge devices. This article presents the recent advances in FL-based training for physical layer design problems. Compared to CL, the effectiveness of FL is presented in terms of communication overhead with a slight performance loss in the learning accuracy. The design challenges, such as model, data, and hardware complexity, are also discussed in detail along with possible solutions.en_US
dc.identifier.doi10.1109/MCOM.101.2100138
dc.identifier.endpage87en_US
dc.identifier.issn0163-6804
dc.identifier.issn1558-1896
dc.identifier.issue11en_US
dc.identifier.scopus2-s2.0-85122215537en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage81en_US
dc.identifier.urihttps://doi.org/10.1109/MCOM.101.2100138
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13007
dc.identifier.volume59en_US
dc.identifier.wosWOS:000736740400028en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorElbir, Ahmet M.
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Communications Magazineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectWireless Communication; Training Data; Performance Evaluation; Image Edge Detection; Computational Modeling; Distributed Databases; Physical Layeren_US
dc.titleFederated Learning for Physical Layer Designen_US
dc.typeArticleen_US

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