Federated Incomplete Multi-View Clustering with Heterogeneous Graph Neural Networks

TL;DR

This paper introduces FIM-GNNs, a federated clustering framework that effectively handles incomplete, heterogeneous multi-view data across distributed devices using graph neural networks and pseudo-labels.

Contribution

It proposes a novel federated multi-view clustering method with heterogeneous GNNs and pseudo-labels to address data heterogeneity and incompleteness in privacy-preserving settings.

Findings

Outperforms state-of-the-art algorithms on benchmark datasets.

Effectively handles incomplete multi-view data.

Enhances clustering accuracy with pseudo-label guidance.

Abstract

Federated multi-view clustering offers the potential to develop a global clustering model using data distributed across multiple devices. However, current methods face challenges due to the absence of label information and the paramount importance of data privacy. A significant issue is the feature heterogeneity across multi-view data, which complicates the effective mining of complementary clustering information. Additionally, the inherent incompleteness of multi-view data in a distributed setting can further complicate the clustering process. To address these challenges, we introduce a federated incomplete multi-view clustering framework with heterogeneous graph neural networks (FIM-GNNs). In the proposed FIM-GNNs, autoencoders built on heterogeneous graph neural network models are employed for feature extraction of multi-view data at each client site. At the server level, heterogeneous features from overlapping samples of each client are aggregated into a global feature representation. Global pseudo-labels are generated at the server to enhance the handling of incomplete view data, where these labels serve as a guide for integrating and refining the clustering process across different data views. Comprehensive experiments have been conducted on public benchmark datasets to verify the performance of the proposed FIM-GNNs in comparison with state-of-the-art algorithms.