Federated Incomplete Multi-View Clustering with Heterogeneous Graph Neural Networks

• URL: http://arxiv.org/abs/2406.08524v1
• Date: Wed, 12 Jun 2024 07:16:00 GMT
• Title: Federated Incomplete Multi-View Clustering with Heterogeneous Graph Neural Networks
• Authors: Xueming Yan, Ziqi Wang, Yaochu Jin,
• Abstract summary: Federated multi-view clustering offers the potential to develop a global clustering model using data distributed across multiple devices. Current methods face challenges due to the absence of label information and the paramount importance of data privacy. We introduce a federated incomplete multi-view clustering framework with heterogeneous graph neural networks (FIM-GNNs)
• Score: 21.710283538891968
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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