{"type":"ScholarlyArticle","title":"Bridging Inter-View and Client Heterogeneity: Federated Multi-View Clustering Under Non-IID Data","authors":[{"name":"Jiazhen Wang","slug":"jiazhen-wang","given":"Jiazhen","family":"Wang","affiliation":""},{"name":"Xinyue Chen","slug":"xinyue-chen","given":"Xinyue","family":"Chen","affiliation":""},{"name":"Shuaiyu Liu","slug":"shuaiyu-liu","given":"Shuaiyu","family":"Liu","affiliation":""},{"name":"Zican He","slug":"zican-he","given":"Zican","family":"He","affiliation":""},{"name":"Yazhou Ren","slug":"yazhou-ren","given":"Yazhou","family":"Ren","affiliation":""},{"name":"Yi Wang","slug":"yi-wang","given":"Yi","family":"Wang","affiliation":""},{"name":"Shuyin Xia","slug":"shuyin-xia","given":"Shuyin","family":"Xia","affiliation":""}],"abstract":"Federated multi-view clustering (FedMVC) has been widely used to discover latent structures in distributed multi-view data, yet most methods still rely on the restrictive assumption of independent and identically distributed (IID) data. In practice, non-IID distributions with partial and imbalanced categories cause clients to learn biased and 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 (H²-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 at https://github.com/JiazhenWang-0522/H2-FedMVC.","doi":"10.24963/ijcai.2026/551","url":"https://ijcai.org/proceedings/2026/551","pdf":"https://ijcai.org/proceedings/2026/0551.pdf","markdown":"https://ijcai.org/proceedings/2026/551.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/551","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Machine Learning","track":"Main Track","number":551,"pages":{"first":4949,"last":4957},"keywords":["Machine Learning: Clustering","Machine Learning: Federated learning","Machine Learning: Multi-view learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}