Beyond View Heterogeneity: Federated Incomplete Multi-View Clustering with Arbitrary View Subsets

Federated multi-view clustering (FedMVC) learns cluster structures from multi-view data distributed across clients without centralizing raw data. Beyond differences in view-specific feature spaces and client view subsets, individual samples may lack views available at their client. Client-level view heterogeneity and sample-level view missingness jointly determine the cross-view supervision and relational evidence available for learning common representations. In this paper, we propose Federated Incomplete Multi-view Clustering via Prediction and Alignment (FIMCPA) for this more general setting. Specifically, masked cross-view predictive pretraining exploits partial joint observations to capture shared information for common representation initialization. Relation-verified contrastive alignment balances sample contributions and uses shared-view agreement to mitigate potential false-negative repulsion, promoting consistency among common and observed-view representations. Observation-weighted aggregation further accounts for differences in available data during model aggregation. These designs allow clients with different observation patterns to contribute to common representation learning without imputing genuinely missing views. Theoretical analysis provides insight into relation verification and its effects on the contrastive objective. Extensive experiments demonstrate superior clustering performance, with further analyses supporting the effectiveness of FIMCPA under varying observation conditions and client compositions.