Fed-AGA: An Anchor Graph Alignment Framework for Federated Unaligned Multi-view Clustering

Federated Multi-view Clustering (FMVC) enables privacy-preserving cross-view fusion from distributed multi-view data, where each client holds one view of samples and participates in the server's fusion without exposing raw data. In practice, existing FMVC methods face two key challenges: (1) the collected samples often show unaligned correspondence across views, especially under distributed collection; and (2) the gap between higher data privacy and lower computation cost remains hard to reconcile. To address these issues, we propose an Anchor Graph Alignment Framework for Federated Unaligned Multi-view Clustering (Fed-AGA), which employs a coarse-to-fine alignment strategy on lightweight anchor graphs to achieve misalignment-robust cross-view fusion while bridging the gap between data privacy and computation cost. Specifically, in the coarse alignment stage, we introduce an anchor-based graph generation module to generate an anchor graph on each client and upload them to the server for category-wise alignment by a designed topology-aware alignment module, which distills topology components as category-exclusive signatures to guide and achieve ideal category-wise alignment. In the next fine alignment and fusion stage, we design an attention-based alignment module to encourage each sample to pay attention to highly similar samples for sample-wise alignment, and the fine-aligned results are contrastively fused into a cross-view consistent anchor graph for clustering. Extensive experiments on multiple datasets demonstrate that Fed-AGA achieves state-of-the-art performance among related methods, while theoretical analysis offers theoretical motivation for two key components.