# Federated Incomplete Multi-View Clustering with Heterogeneous Graph Neural Networks

arXiv: https://arxiv.org/abs/2406.08524 · HTML: https://mlsanity.com/paper/2406.08524/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 12 Jun 2024.

## Builds on (most cited first, 7 of 7)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Graph Clustering with Graph Neural Networks](https://mlsanity.com/paper/2006.16904/) (2020)
- [Attributed Graph Clustering: A Deep Attentional Embedding Approach](https://mlsanity.com/paper/1906.06532/) (2019)
- [Deep Clustering: A Comprehensive Survey](https://mlsanity.com/paper/2210.04142/) (2022)
- [A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations](https://mlsanity.com/paper/2008.10808/) (2020)
- [CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network](https://mlsanity.com/paper/2403.19514/) (2024)
- [Federated Deep Multi-View Clustering with Global Self-Supervision](https://mlsanity.com/paper/2309.13697/) (2023)

## Built on by (most cited first, 2 of 2)

- [Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance](https://mlsanity.com/paper/2506.15703/) (2025)
- [Distributed clustering in partially overlapping feature spaces](https://mlsanity.com/paper/2510.09799/) (2025)

## Abstract (arXiv)

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.