A Vertical Federated Multi-View Fuzzy Clustering Method for Incomplete Data

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorLi, Yan
dc.contributor.authorHu, Xingchen
dc.contributor.authorYu, Shengju
dc.contributor.authorDing, Weiping
dc.contributor.authorPedrycz, Witold
dc.contributor.authorKiat, Yeo Chai
dc.contributor.authorLiu, Zhong
dc.date.accessioned2025-04-18T09:46:36Z
dc.date.available2025-04-18T09:46:36Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractMulti-view fuzzy clustering (MVFC) has gained widespread adoption owing to its inherent flexibility in handling ambiguous data. The proliferation of privatization devices has driven the emergence of new challenge in MVFC researches. Federated learning, a technique that can jointly train without directly using raw data, has gain significant attention in decentralized MVFC. However, their applicability depends on the assumptions of data integrity and independence between different views. In fact, while within distributed environments, data typically exhibits two challenging problems: (1) multiple views within a single client; (2) incomplete data. Existing methods exhibit limitations in effectively addressing these challenges. Hence, in this study, we aim at achieving the effective clustering for incomplete data by a novel vertical federated MVFC framework. Specifically, a unified clustering framework is designed to capture both local client learning and global server training. For the local client learning, the data reconstruction strategy and prototype alignment strategy are introduced to ensure the preservation of data structure and refinement of clustering relationships, which mitigates the impact of incomplete data. Meanwhile, the global training process implements aggregation based on client-specific information. The whole process is realized based on the unified fuzzy clustering framework, promoting collaborative learning between client-specific and server information. Theoretical analyses and extensive experiments are carefully conducted to validate the effectiveness and efficiency of the proposed method from multiple perspectives. © 1993-2012 IEEE.
dc.identifier.citationLi, Y., Hu, X., Yu, S., Ding, W., Pedrycz, W., Kiat, Y. C., & Liu, Z. (2025). A Vertical Federated Multi-View Fuzzy Clustering Method for Incomplete Data. IEEE Transactions on Fuzzy Systems.
dc.identifier.doi10.1109/TFUZZ.2025.3526978
dc.identifier.issn10636706
dc.identifier.scopus2-s2.0-85214658423
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.1109/TFUZZ.2025.3526978
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6856
dc.indekslendigikaynakScopus
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIEEE Transactions on Fuzzy Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectData Reconstruction
dc.subjectFederated Learning
dc.subjectFuzzy Clustering
dc.subjectİncomplete Multi-view Clustering
dc.titleA Vertical Federated Multi-View Fuzzy Clustering Method for Incomplete Data
dc.typeArticle

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