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.

通过引入具有异质图神经网络的不完整多视图聚类框架（FIM-GNNs），我们解决了由于缺乏标签信息和数据隐私的重要性而导致的异构特征数据的有效挖掘困难，以及分布式环境中多视图数据的固有不完整性使得聚类过程复杂化的问题。

异构图神经网络下的联邦多视角聚类

Federated Incomplete Multi-View Clustering with Heterogeneous Graph  Neural Networks

Federated multi-view clustering has the potential to learn a global
clustering model from data distributed across multiple devices. In this
setting, label information is unknown and data privacy must be preserved,
leading to two major challenges. First, views on different clients often have
feature heterogeneity, and mining their complementary cluster information is
not trivial. Second, the storage and usage of data from multiple clients in a
distributed environment can lead to incompleteness of multi-view data. To
address these challenges, we propose a novel federated deep multi-view
clustering method that can mine complementary cluster structures from multiple
clients, while dealing with data incompleteness and privacy concerns.
Specifically, in the server environment, we propose sample alignment and data
extension techniques to explore the complementary cluster structures of
multiple views. The server then distributes global prototypes and global
pseudo-labels to each client as global self-supervised information. In the
client environment, multiple clients use the global self-supervised information
and deep autoencoders to learn view-specific cluster assignments and embedded
features, which are then uploaded to the server for refining the global
self-supervised information. Finally, the results of our extensive experiments
demonstrate that our proposed method exhibits superior performance in
addressing the challenges of incomplete multi-view data in distributed
environments.

提出一种新颖的联合深度多视图聚类方法，能够从分布在多个设备上的数据中挖掘互补的聚类结构，同时解决数据不完整性和隐私问题。在服务器环境中，使用样本对齐和数据扩展技术来探索多个视图的互补聚类结构。然后，服务器将全局原型和全局伪标签分配给每个客户端作为全局自监督信息。在客户端环境中，多个客户端使用全局自监督信息和深度自编码器学习视图特定的聚类分配和嵌入特征，然后上传到服务器以进一步完善全局自监督信息。最后，广泛的实验证明，我们提出的方法在处理分布式环境中的不完整多视图数据方面具有优越的性能。