Privacy-Preserving Realization of Fuzzy Clustering and Fuzzy Modeling Through Vertical Federated Learning

Basit Öğe Kaydı
dc.authoridZhu, Xiubin/0000-0002-7947-8749
dc.contributor.authorZhu, Xiubin
dc.contributor.authorWang, Dan
dc.contributor.authorPedrycz, Witold
dc.contributor.authorLi, Zhiwu
dc.date.accessioned2024-05-19T14:40:41Z
dc.date.available2024-05-19T14:40:41Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractIn this study, we elaborate on a realization of fuzzy clustering and the construction of fuzzy rule-based models on the basis of vertically partitioned datasets in a privacy-preserving federated learning approach. The main focus of the overall design process is to construct a family of information granules (clusters) and the corresponding fuzzy rules in the presence of a collection of vertically partitioned datasets without compromising data privacy. These datasets are composed of the same data but are described by different features, and due to security considerations, data cannot be shared. The vertical federated fuzzy clustering can be realized as an iterative optimization process composed of successive cycles: 1) computation (update) of the prototypes and partition matrices performed on the basis of local datasets and 2) an integration of the local sources of knowledge carried out on a central coordinator-server. The update of the partition matrices can be completed using a distance-based or gradient-based approach. The communication of findings between local clients and the coordinator-server is realized through exchanging partition matrices, which are more general than numeric data and can avoid leakage of data privacy. Fuzzy models are optimized in a similar manner through exchanging the gradients of the performance index computed with respect to the parameters between the clients and the global coordinator. The proposed mechanism exhibits significant originality since the realization of fuzzy modeling in a vertical federated learning environment has not been studied. Experimental studies show that the proposed federated clustering and fuzzy model design could effectively reveal the structure of the entire dataset and achieve high performance compared with the results obtained in a centralized manner.en_US
dc.description.sponsorshipNational Natural Science Foundation of China [62076189, 62302364, 62006184]; Recruitment Program of Global Experts;; Canada Research Chair (CRC); Natural Sciences and Engineering Research Council of Canada (NSERC); Science and Technology Development Fund, MSAR [0012/2019/A1]en_US
dc.description.sponsorshipThis work was supported in part by the National Natural Science Foundation of China under Grant 62076189, Grant 62302364, and Grant 62006184; in part by the Recruitment Program of Global Experts; in part by the Canada Research Chair (CRC); in part by the Natural Sciences and Engineering Research Council of Canada (NSERC); and in part by the Science and Technology Development Fund, MSAR, under Grant 0012/2019/A1.en_US
dc.identifier.doi10.1109/TSMC.2023.3320680
dc.identifier.endpage924en_US
dc.identifier.issn2168-2216
dc.identifier.issn2168-2232
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85174859506en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage915en_US
dc.identifier.urihttps://doi.org10.1109/TSMC.2023.3320680
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5001
dc.identifier.volume54en_US
dc.identifier.wosWOS:001097550400001en_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 Systems Man Cybernetics-Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectData Privacy And Confidentialityen_US
dc.subjectFuzzy Clusteringen_US
dc.subjectFuzzy Rule-Based Modelen_US
dc.subjectPartition Matrixen_US
dc.subjectVertical Federated Learningen_US
dc.titlePrivacy-Preserving Realization of Fuzzy Clustering and Fuzzy Modeling Through Vertical Federated Learningen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu