Heterogeneity-Aware Federated Graph Neural Networks for Incomplete Multi-View Clustering

Federated multi-view clustering aims to collaboratively learn a global clustering model from decentralized and privacy-sensitive data distributed across multiple clients. However, existing approaches face two major challenges: the absence of supervision signals and the heterogeneity across multi-view features, which hinder the extraction of consistent and complementary clustering information. Moreover, the inherent incompleteness of multi-view data in federated scenarios further complicates the learning process. To tackle these issues, we propose a Heterogeneity-aware Federated Graph Neural Networks (HafGNN) for the incomplete multi-view clustering. HafGNN employs heterogeneous graph neural network-based autoencoders for the different clients to capture both view-specific and structural representations while preserving data locality. A server-side aggregation mechanism aligns heterogeneous features from overlapping instances to construct a global latent representation. Additionally, global pseudo-labels are generated to guide view completion and enhance clustering consistency across clients. Extensive experiments on multiple public multi-view datasets demonstrate that HafGNN consistently outperforms state-of-the-art approaches in clustering performance, especially under conditions of view incompleteness and client heterogeneity.