  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.
