Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance

• URL: http://arxiv.org/abs/2506.15703v1
• Date: Fri, 30 May 2025 02:17:51 GMT
• Title: Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance
• Authors: Guoqing Chao, Zhenghao Zhang, Lei Meng, Jie Wen, Dianhui Chu,
• Abstract summary: We propose a novel Federated Incomplete Multi-view Clustering method with globally Fused Graph guidance (FIMCFG)<n>Under the guidance of the fused graph, the two underlying features are fused into high-level features, based on which clustering is conducted under the supervision of pseudo-labeling.
• Score: 10.443422245356922
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated multi-view clustering has been proposed to mine the valuable information within multi-view data distributed across different devices and has achieved impressive results while preserving the privacy. Despite great progress, most federated multi-view clustering methods only used global pseudo-labels to guide the downstream clustering process and failed to exploit the global information when extracting features. In addition, missing data problem in federated multi-view clustering task is less explored. To address these problems, we propose a novel Federated Incomplete Multi-view Clustering method with globally Fused Graph guidance (FIMCFG). Specifically, we designed a dual-head graph convolutional encoder at each client to extract two kinds of underlying features containing global and view-specific information. Subsequently, under the guidance of the fused graph, the two underlying features are fused into high-level features, based on which clustering is conducted under the supervision of pseudo-labeling. Finally, the high-level features are uploaded to the server to refine the graph fusion and pseudo-labeling computation. Extensive experimental results demonstrate the effectiveness and superiority of FIMCFG. Our code is publicly available at https://github.com/PaddiHunter/FIMCFG.

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