Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance

TL;DR

This paper introduces FIMCFG, a federated multi-view clustering method that effectively handles incomplete data and leverages globally fused graph guidance to improve clustering accuracy while preserving privacy.

Contribution

The paper proposes a novel federated clustering approach with a dual-head graph encoder and global graph fusion, addressing data incompleteness and enhancing feature extraction.

Findings

FIMCFG outperforms existing methods in clustering accuracy.

The method effectively handles incomplete multi-view data.

Global graph fusion improves feature representation and clustering results.

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.