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

Abstract 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.