# Federated K-means Clustering

arXiv: https://arxiv.org/abs/2310.01195 · HTML: https://mlsanity.com/paper/2310.01195/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 2 Oct 2023.

## Builds on (most cited first, 6 of 6)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [EMNIST: an extension of MNIST to handwritten letters](https://mlsanity.com/paper/1702.05373/) (2017)
- [Heterogeneity for the Win: One-Shot Federated Clustering](https://mlsanity.com/paper/2103.00697/) (2021)

## Built on by (most cited first, 9 of 9)

- [Representation learning to advance multi-institutional studies with electronic health record data from US and France](https://mlsanity.com/paper/2502.08547/) (2025)
- [Dynamically Weighted Federated k-Means](https://mlsanity.com/paper/2310.14858/) (2023)
- [Federated Variational Inference for Bayesian Mixture Models](https://mlsanity.com/paper/2502.12684/) (2025)
- [Distributed clustering in partially overlapping feature spaces](https://mlsanity.com/paper/2510.09799/) (2025)
- [Insights into the Unknown: Federated Data Diversity Analysis on Molecular Data](https://mlsanity.com/paper/2510.19535/) (2025)
- [Federated Multi-Task Clustering](https://mlsanity.com/paper/2512.22897/) (2025)
- [A Behaviour-Aware Federated Forecasting Framework for Distributed Stand-Alone Wind Turbines](https://mlsanity.com/paper/2603.05263/) (2026)
- [Variational Consensus Monte Carlo for Bayesian Mixture](https://mlsanity.com/paper/2606.19643/) (2026)
- [Federated Clustering with Unknown Local and Global Cluster Cardinalities](https://mlsanity.com/paper/2609.36762/) (2026)

## Abstract (arXiv)

Federated learning is a technique that enables the use of distributed datasets for machine learning purposes without requiring data to be pooled, thereby better preserving privacy and ownership of the data. While supervised FL research has grown substantially over the last years, unsupervised FL methods remain scarce. This work introduces an algorithm which implements K-means clustering in a federated manner, addressing the challenges of varying number of clusters between centers, as well as convergence on less separable datasets.