# Towards Federated Clustering: A Federated Fuzzy c-Means Algorithm (FFCM)

arXiv: https://arxiv.org/abs/2201.07316 · HTML: https://mlsanity.com/paper/2201.07316/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 33 references that are in the map.
- First posted on arXiv 18 Jan 2022.

## Builds on (most cited first, 15 of 15)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [An Efficient Framework for Clustered Federated Learning](https://mlsanity.com/paper/2006.04088/) (2020)
- [Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints](https://mlsanity.com/paper/1910.01991/) (2019)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2009.13012/) (2020)
- [Heterogeneity for the Win: One-Shot Federated Clustering](https://mlsanity.com/paper/2103.00697/) (2021)
- [In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning](https://mlsanity.com/paper/1809.07857/) (2018)
- [Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applications](https://mlsanity.com/paper/1907.01132/) (2019)
- [Practical Federated Gradient Boosting Decision Trees](https://mlsanity.com/paper/1911.04206/) (2019)
- [Cluster-driven Graph Federated Learning over Multiple Domains](https://mlsanity.com/paper/2104.14628/) (2021)
- [Dynamic Clustering in Federated Learning](https://mlsanity.com/paper/2012.03788/) (2020)
- [A Federated Learning Approach for Mobile Packet Classification](https://mlsanity.com/paper/1907.13113/) (2019)

## Built on by (most cited first, 30 of 30)

- [A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy](https://mlsanity.com/paper/2302.10637/) (2023)
- [FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering](https://mlsanity.com/paper/2301.12379/) (2023)
- [Training Machine Learning models at the Edge: A Survey](https://mlsanity.com/paper/2403.02619/) (2024)
- [Asynchronous Federated Clustering with Unknown Number of Clusters](https://mlsanity.com/paper/2412.20341/) (2024)
- [Differentially Private Federated Clustering over Non-IID Data](https://mlsanity.com/paper/2301.00955/) (2023)
- [FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data](https://mlsanity.com/paper/2207.10265/) (2022)
- [Federated Deep Multi-View Clustering with Global Self-Supervision](https://mlsanity.com/paper/2309.13697/) (2023)
- [StoCFL: A Stochastically Clustered Federated Learning Framework for Non-IID Data with Dynamic Client Participation](https://mlsanity.com/paper/2303.00897/) (2023)
- [CCFC: Bridging Federated Clustering and Contrastive Learning](https://mlsanity.com/paper/2401.06634/) (2024)
- [Federated clustering with GAN-based data synthesis](https://mlsanity.com/paper/2210.16524/) (2022)
- [Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologies](https://mlsanity.com/paper/2310.05397/) (2023)
- [CCFC++: Enhancing Federated Clustering through Feature Decorrelation](https://mlsanity.com/paper/2402.12852/) (2024)
- [A Bayesian Framework for Clustered Federated Learning](https://mlsanity.com/paper/2410.15473/) (2024)
- [Federated K-Means Clustering via Dual Decomposition-based Distributed Optimization](https://mlsanity.com/paper/2307.13267/) (2023)
- [Dynamically Weighted Federated k-Means](https://mlsanity.com/paper/2310.14858/) (2023)
- [Distributed Collapsed Gibbs Sampler for Dirichlet Process Mixture Models in Federated Learning](https://mlsanity.com/paper/2312.11169/) (2023)
- [Fair Federated Data Clustering through Personalization: Bridging the Gap between Diverse Data Distributions](https://mlsanity.com/paper/2407.04302/) (2024)
- [Jigsaw Game: Federated Clustering](https://mlsanity.com/paper/2407.12764/) (2024)
- [Federated Variational Inference for Bayesian Mixture Models](https://mlsanity.com/paper/2502.12684/) (2025)
- [FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning](https://mlsanity.com/paper/2506.21054/) (2025)
- [A Federated Generalized Expectation-Maximization Algorithm for Mixture Models with an Unknown Number of Components](https://mlsanity.com/paper/2601.21160/) (2026)
- [Federated Learning for Short Text Clustering](https://mlsanity.com/paper/2312.07556/) (2023)
- [Architectural Blueprint For Heterogeneity-Resilient Federated Learning](https://mlsanity.com/paper/2403.04546/) (2024)
- [Federated One-Shot Ensemble Clustering](https://mlsanity.com/paper/2409.08396/) (2024)
- [Federated Deep Subspace Clustering](https://mlsanity.com/paper/2501.00230/) (2024)
- [OmniFC: Rethinking Federated Clustering via Lossless and Secure Distance Reconstruction](https://mlsanity.com/paper/2505.13071/) (2025)
- [LSHFed: Robust and Communication-Efficient Federated Learning with Locally-Sensitive Hashing Gradient Mapping](https://mlsanity.com/paper/2511.01296/) (2025)
- [One-Shot Federated Clustering of Non-Independent Completely Distributed Data](https://mlsanity.com/paper/2601.17512/) (2026)
- [Federated Clustering with Unknown Local and Global Cluster Cardinalities](https://mlsanity.com/paper/2609.36762/) (2026)
- [Fed-BRDECS: Privacy-Preserving and Heterogeneity-Aware Federated Deep Embedded Clustering](https://mlsanity.com/paper/2610.07399/) (2026)

## Abstract (arXiv)

Federated Learning (FL) is a setting where multiple parties with distributed data collaborate in training a joint Machine Learning (ML) model while keeping all data local at the parties. Federated clustering is an area of research within FL that is concerned with grouping together data that is globally similar while keeping all data local. We describe how this area of research can be of interest in itself, or how it helps addressing issues like non-independently-identically-distributed (i.i.d.) data in supervised FL frameworks. The focus of this work, however, is an extension of the federated fuzzy c-means algorithm to the FL setting (FFCM) as a contribution towards federated clustering. We propose two methods to calculate global cluster centers and evaluate their behaviour through challenging numerical experiments. We observe that one of the methods is able to identify good global clusters even in challenging scenarios, but also acknowledge that many challenges remain open.