Scalable Federated One-Step Multi-View Clustering with Tensorized Regularization

Multi-view clustering (MVC) methods have garnered considerable attention within centralized data frameworks. However, real-world multi-view data are often collected and stored by different organizations, complicating the practical deployment of MVC and motivating the emergence of federated multi-view clustering (FMVC). Existing FMVC approaches typically necessitate post-processing to derive clustering labels and confront challenges in effectively exploring the complementary and consistent information across multi-view data residing in different entities. To address these limitations, we propose a novel framework termed Scalable Federated One-Step Multi-View Clustering with Tensorized Regularization (SFOMVC-TR). This framework facilitates one-step clustering at each client and employs tensor learning to capture consistent and complementary information through a centralized server. Additionally, it adopts anchor graphs to enhance clustering efficiency and scalability in high-dimensional data. By incorporating a Lp,q sparse regularization on the projection matrix, SFOMVC-TR enables the direct projection of anchors into clustering assignments to mitigate redundancy. A federated optimization framework is developed to support collaborative and privacy-preserving training under the coordination of the server. Extensive experiments on multiple datasets validate the privacy and effectiveness of our method.

Authors

• Wei Feng Xi'an Jiaotong University
• Danting Liu Xi'an Jiaotong University
• Qianqian Wang Xidian University
• Wenqi Liang State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences. University of Chinese Academy of Sciences.
• Zheng Yan Xidian University

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