A Federated K-Means-Based Approach in eHealth Domains with Heterogeneous Data Distributions

Giovanni Paragliola, Patrizia Ribino, Maria Mannone, Maria Mannone

2024

Abstract

Healthcare organizations collect and store significant amounts of patient health information. However, sharing or accessing this information outside of their facilities is often hindered by factors such as privacy concerns. Federated Learning (FL) data systems are emerging to overcome the siloed nature of health data and the barriers to sharing it. While federated approaches have been extensively studied, especially in classification problems, clustering-oriented approaches are still relatively few and less widespread, both in formulating algorithms and in their application in eHealth domains. The primary objective of this paper is to introduce a federated K-means-based approach for clustering tasks within the healthcare domain and explore the impact of heterogeneous health data distributions. The evaluation of the proposed federated K-means approach has been conducted on several health-related datasets through comparison with the centralized version and by estimating the trade-off between privacy and performance. The preliminary findings suggest that in the case of heterogeneous health data distributions, the difference between the centralized and federated approach is marginal, with the federated approach outperforming the centralized one on some healthcare datasets.

Paper Citation

in Harvard Style

Paragliola G., Ribino P. and Mannone M. (2024). A Federated K-Means-Based Approach in eHealth Domains with Heterogeneous Data Distributions. In Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA; ISBN 978-989-758-721-4, SciTePress, pages 550-559. DOI: 10.5220/0012981200003837

in Bibtex Style

@conference{ncta24,
author={Giovanni Paragliola and Patrizia Ribino and Maria Mannone},
title={A Federated K-Means-Based Approach in eHealth Domains with Heterogeneous Data Distributions},
booktitle={Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA},
year={2024},
pages={550-559},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012981200003837},
isbn={978-989-758-721-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA
TI - A Federated K-Means-Based Approach in eHealth Domains with Heterogeneous Data Distributions
SN - 978-989-758-721-4
AU - Paragliola G.
AU - Ribino P.
AU - Mannone M.
PY - 2024
SP - 550
EP - 559
DO - 10.5220/0012981200003837
PB - SciTePress