Federated K-means Clustering
Machine Learning
2024-02-19 v2 Distributed, Parallel, and Cluster Computing
Abstract
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.
Cite
@article{arxiv.2310.01195,
title = {Federated K-means Clustering},
author = {Swier Garst and Marcel Reinders},
journal= {arXiv preprint arXiv:2310.01195},
year = {2024}
}