# Federated Deep Multi-View Clustering with Global Self-Supervision

arXiv: https://arxiv.org/abs/2309.13697 · HTML: https://mlsanity.com/paper/2309.13697/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 50 references that are in the map.
- First posted on arXiv 24 Sep 2023.

## Builds on (most cited first, 20 of 20)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Practical Secure Aggregation for Federated Learning on User-Held Data](https://mlsanity.com/paper/1611.04482/) (2016)
- [Heterogeneity for the Win: One-Shot Federated Clustering](https://mlsanity.com/paper/2103.00697/) (2021)
- [Deep Partial Multi-View Learning](https://mlsanity.com/paper/2011.06170/) (2020)
- [Cluster-guided Contrastive Graph Clustering Network](https://mlsanity.com/paper/2301.01098/) (2023)
- [Multi-Participant Multi-Class Vertical Federated Learning](https://mlsanity.com/paper/2001.11154/) (2020)
- [From Federated Learning to Federated Neural Architecture Search: A Survey](https://mlsanity.com/paper/2009.05868/) (2020)
- [Self-Supervised Discriminative Feature Learning for Deep Multi-View Clustering](https://mlsanity.com/paper/2103.15069/) (2021)
- [Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data](https://mlsanity.com/paper/2206.08330/) (2022)
- [Deep Multimodal Subspace Clustering Networks](https://mlsanity.com/paper/1804.06498/) (2018)
- [Towards Federated Clustering: A Federated Fuzzy c-Means Algorithm (FFCM)](https://mlsanity.com/paper/2201.07316/) (2022)
- [Federated Multi-view Matrix Factorization for Personalized Recommendations](https://mlsanity.com/paper/2004.04256/) (2020)
- [Interpolation-based Contrastive Learning for Few-Label Semi-Supervised Learning](https://mlsanity.com/paper/2202.11915/) (2022)
- [Iterative Views Agreement: An Iterative Low-Rank based Structured Optimization Method to Multi-View Spectral Clustering](https://mlsanity.com/paper/1608.05560/) (2016)
- [A Systematic Literature Review on Federated Learning: From A Model Quality Perspective](https://mlsanity.com/paper/2012.01973/) (2020)
- [Feature Concatenation Multi-view Subspace Clustering](https://mlsanity.com/paper/1901.10657/) (2019)
- [A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations](https://mlsanity.com/paper/2008.10808/) (2020)
- [Federated Multi-View Learning for Private Medical Data Integration and Analysis](https://mlsanity.com/paper/2105.01603/) (2021)
- [A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations](https://mlsanity.com/paper/2204.07352/) (2022)

## Built on by (most cited first, 5 of 5)

- [Federated Incomplete Multi-View Clustering with Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2406.08524/) (2024)
- [Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance](https://mlsanity.com/paper/2506.15703/) (2025)
- [Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views](https://mlsanity.com/paper/2410.09484/) (2024)
- [Distributed clustering in partially overlapping feature spaces](https://mlsanity.com/paper/2510.09799/) (2025)
- [Enhanced Federated Deep Multi-View Clustering under Uncertainty Scenario](https://mlsanity.com/paper/2511.17631/) (2025)

## Abstract (arXiv)

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.