Adaptive Semi-Supervised Federated Multi-View Clustering

Federated multi-view clustering enables collaborative clustering of multi-view data distributed across isolated clients while preserving data privacy. However, existing methods largely rely on unsupervised learning, which may overlook available prior information and consequently limit the discriminability of local representations and the reliability of global consensus. To address this issue, we introduce supervisory information into clustering process and propose Adaptive Semi-Supervised Federated Multi-View Clustering (ASFMVC). Specifically, to reduce the dependence on intrinsic data structure alone, we introduce a label-guided representation generation mechanism that incorporates the observed labels into representation learning. Since different views may provide clustering structures of unequal quality, we further design a consensus-aware adaptive weighting strategy to adjust their contributions according to their discrepancies from the current consensus. Moreover, the same supervision is transformed into signed pairwise relations to guide consensus refinement through must-link and cannot-link constraints. These components are integrated into a unified alternating optimization framework with compact communication and of linear complexity. Experiments on six benchmark datasets demonstrate the effectiveness of the proposed method.