Federated Incomplete Multi-View Clustering with Heterogeneous Graph Neural Networks
<|reference_start|>Federated Incomplete Multi-View Clustering with Heterogeneous Graph Neural Networks: 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.<|reference_end|>
@article{yan2024federated,
    title={Federated Incomplete Multi-View Clustering with Heterogeneous Graph
  Neural Networks},
    author={Xueming Yan, Ziqi Wang, Yaochu Jin},
    journal={arXiv preprint arXiv:2406.08524},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08524},
    primaryClass={cs.LG cs.DC}
}