An Efficient Federated Multiview Fuzzy C-Means Clustering Method

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dc.authoridLiu, Jiyuan/0000-0001-5702-4941
dc.authoridLIU, Xinwang/0000-0001-9066-1475
dc.contributor.authorHu, Xingchen
dc.contributor.authorQin, Jindong
dc.contributor.authorShen, Yinghua
dc.contributor.authorPedrycz, Witold
dc.contributor.authorLiu, Xinwang
dc.contributor.authorLiu, Jiyuan
dc.date.accessioned2024-05-19T14:46:20Z
dc.date.available2024-05-19T14:46:20Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractMultiview clustering has been received considerable attention due to the widespread collection of multiview data from diverse domains and sources. However, storing multiview data across multiple devices in many real scenarios poses significant challenges for efficient data analysis. Federated learning framework enables collaborative machine learning on distributed devices while preserving privacy constraints. Even though there have been intensive algorithms on multiview fuzzy clustering, federated multiview fuzzy clustering has not been adequately investigated so far. In this study, we first develop the federated learning mode into multiview fuzzy clustering and realize the federated optimization procedure, called federated multiview fuzzy C-means clustering. Then, we design an original strategy of consensus prototype learning during federated multiview fuzzy clustering. It is termed as federated multiview fuzzy C-means consensus prototypes clustering (FedMVFPC). We also further develop the federated alternative optimization algorithm with proven convergence. This study also introduces the notion of clustering prototype communication within the federated learning framework, and integrates the clustering prototypes of different views into a unified optimization formulation. The experimental studies on various benchmark datasets demonstrate that the proposed FedMVFPC method improves the federated clustering performance and efficiency. It achieves comparable or better clustering performance against the existing state-of-the-art multiview clustering algorithms.en_US
dc.description.sponsorshipNational Natural Science Foundation of China (NSFC)en_US
dc.description.sponsorshipNo Statement Availableen_US
dc.identifier.doi10.1109/TFUZZ.2023.3335361
dc.identifier.endpage1899en_US
dc.identifier.issn1063-6706
dc.identifier.issn1941-0034
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85179090166en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1886en_US
dc.identifier.urihttps://doi.org10.1109/TFUZZ.2023.3335361
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5500
dc.identifier.volume32en_US
dc.identifier.wosWOS:001196731700070en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Transactions on Fuzzy Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectPrototypesen_US
dc.subjectFuzzy Logicen_US
dc.subjectClustering Algorithmsen_US
dc.subjectClustering Methodsen_US
dc.subjectOptimizationen_US
dc.subjectFederated Learningen_US
dc.subjectDistributed Databasesen_US
dc.subjectFuzzy Clusteringen_US
dc.subjectFederated Learningen_US
dc.subjectMultiview Clusteringen_US
dc.subjectDistributed Dataen_US
dc.subjectPrototype Learningen_US
dc.titleAn Efficient Federated Multiview Fuzzy C-Means Clustering Methoden_US
dc.typeArticleen_US

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