A Vertical Federated Multi-View Fuzzy Clustering Method for Incomplete Data
Küçük Resim Yok
Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Multi-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.
Açıklama
Anahtar Kelimeler
Data Reconstruction, Federated Learning, Fuzzy Clustering, İncomplete Multi-view Clustering
Kaynak
IEEE Transactions on Fuzzy Systems
WoS Q Değeri
Scopus Q Değeri
Q1
Cilt
Sayı
Künye
Li, 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.
