Bridging Inter-View and Client Heterogeneity: Federated Multi-View Clustering Under Non-IID Data

Jiazhen Wang; Xinyue Chen; Shuaiyu Liu; Zican He; Yazhou Ren; Yi Wang; Shuyin Xia

IJCAI 20260 citations

Bridging Inter-View and Client Heterogeneity: Federated Multi-View Clustering Under Non-IID Data

Jiazhen Wang, Xinyue Chen, Shuaiyu Liu, Zican He, Yazhou Ren, Yi Wang, Shuyin Xia

Abstract

Federated multi-view clustering (FedMVC) has been widely used to discover latent structures in distributed multi-view data, but most methods assume independent and identically distributed (IID) data. In practice, non-IID distributions with partial and imbalanced categories cause clients to learn biased, local representations, leading to model bias and unstable federated training performance. To address these issues, we propose a FedMVC framework called Bridging Inter-View and Client Heterogeneity: Federated Multi-View Clustering under Non-IID Data (H$^{2}$-FedMVC), which tackles both inter-view and client heterogeneity in mixed-view settings. We propose a leader-guided learning mechanism to address inter-view heterogeneity by enhancing complementary and discriminative feature learning, and an expert adaptive aggregation strategy to handle client heterogeneity by prioritizing well-matched experts in global updates. Theoretical analysis and experiments demonstrate superior performance in heterogeneous mixed-view FedMVC settings, with code provided in the supplementary materials.

Machine Learning: ClusteringMachine Learning: Federated learningMachine Learning: Multi-view learning

BibTeX

@inproceedings{ijcai2026_bridgingintervie,
  title = {Bridging Inter-View and Client Heterogeneity: Federated Multi-View Clustering Under Non-IID Data},
  author = {Jiazhen Wang and Xinyue Chen and Shuaiyu Liu and Zican He and Yazhou Ren and Yi Wang and Shuyin Xia},
  booktitle = {IJCAI 2026},
  year = {2026}
}