文献类型：会议论文

英文题名：Federated Incomplete Multi-view Clustering with Heterogeneous Graph Neural Networks

作者：Yan, Xueming[1,3];Wang, Ziqi[2];Jin, Yaochu[3]

机构：[1]Guangdong Univ Foreign Studies, Guangzhou, Peoples R China;[2]East China Univ Sci & Technol, Shanghai, Peoples R China;[3]Westlake Univ, Sch Engn, Hangzhou, Peoples R China

会议论文集：2024 International Federated Learning Workshops-FL

会议日期：MAY 14, 2024

会议地点：Vancouver, CANADA

语种：英文

外文关键词：Incomplete multi-view clustering; Federated learning; Heterogeneous graph neural networks

摘要：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.