Serverless federated learning: Decentralized spectrum sensing in heterogeneous networks

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dc.contributor.authorCatak, Ferhat Ozgur
dc.contributor.authorKuzlu, Murat
dc.contributor.authorDalveren, Yaser
dc.contributor.authorOzdemir, Gokcen
dc.date.accessioned2025-03-20T09:51:07Z
dc.date.available2025-03-20T09:51:07Z
dc.date.issued2025
dc.departmentİzmir Bakırçay Üniversitesi
dc.description.abstractFederated learning (FL) has gained more popularity due to the increasing demand for robust and efficient mechanisms to ensure data privacy and security during collaborative model training in the concept of artificial intelligence/machine learning (AI/ML). This study proposes an advanced version of FL without the central server, called a serverless or decentralized federated learning framework, to address the challenge of cooperative spectrum sensing in non-independent and identically distributed (non-IID) environments. The framework leverages local model aggregation at neighboring nodes to improve robustness, privacy, and generalizability. The system incorporates weighted aggregation based on distributional similarity between local datasets using Wasserstein distance. The results demonstrate that the proposed serverless federated learning framework offers a satisfactory performance in terms of accuracy and resilience.
dc.identifier.doi10.1016/j.phycom.2025.102634
dc.identifier.issn1874-4907
dc.identifier.scopus2-s2.0-85218407744
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1016/j.phycom.2025.102634
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2426
dc.identifier.volume70
dc.identifier.wosWOS:001434882300001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofPhysical Communication
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250319
dc.subjectFederated learning (FL)
dc.subjectDecentralized FL
dc.subjectNon-IID
dc.subjectSpectrum sensing
dc.titleServerless federated learning: Decentralized spectrum sensing in heterogeneous networks
dc.typeArticle

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