Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance

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 ispublicly available at https://github.com/PaddiHunter/FIMCFG.

When data is spread across different devices, it’s hard to analyze it all together without risking privacy. When some of the data is missing, the existing methods struggle to work well.We designed a smart way to deal with the above problems with a federated learning paradigm, which can cluster the missing data across different devices well.Our method outperforms the existing techniques and ensure the stronger privacy, more accurate clustering performance and missing data handling well. This method is a reliable tool for real-world applications like healthcare or smart devices.