Federated Learning Harnessed with Differential Privacy for Heart Disease Prediction: Enhancing Privacy and Accuracy

Wided Moulahi, Tarek Moulahi, Imen Jdey, Salah Zidi

2025

Abstract

The increasing digitization of healthcare raises the concerns surrounding the patients’ privacy. Therefore, the integration of privacy preserving technologies has proven imperative to curb the negative repercussions tied to technology deployment in the medical sector and to provide trustworthy artificial intelligence healthcare applications. Two raising approaches are promoted to the forefront of research and gaining momentum in the realm of healthcare smart systems: Federated Learning and Differential Privacy. On one hand, Federated Learning (FL) enables collaborative model training across multiple institutions without exchanging raw data. Differential Privacy (DP), on the other hand, provides a formal framework for safeguarding data against potential privacy breaches. The application of these approaches in healthcare settings ensures the protection of sensitive patient informations. In this paper, we delve into the challenges posed by medical data to see how FL and DP can be tailored to suit these requirements. We aim to strike a balance between technology deployment in the medical field and privacy preservation. To this end, we developed a Multi-layer Perceptron (MLP) model to predict if a person is at risk to have heart diseases. The model, trained on different medical datasets for heart diseases, reached an accuracy of 99.57%. The same model was trained in FL framework. It achieved a FL averaged accuracy reaching 99.15%. In a third scenario, to enhance clients’ privacy, we deployed a DP framework. The differentially private MLP achieved an accuracy extending to 97.07% in centralized settings and averaged accuracy attaining 89.94% in FL settings, outperforming existing methods in heart diseases prediction.

Paper Citation

in Harvard Style

Moulahi W., Moulahi T., Jdey I. and Zidi S. (2025). Federated Learning Harnessed with Differential Privacy for Heart Disease Prediction: Enhancing Privacy and Accuracy. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 845-852. DOI: 10.5220/0013188900003890

in Bibtex Style

@conference{icaart25,
author={Wided Moulahi and Tarek Moulahi and Imen Jdey and Salah Zidi},
title={Federated Learning Harnessed with Differential Privacy for Heart Disease Prediction: Enhancing Privacy and Accuracy},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={845-852},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013188900003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Federated Learning Harnessed with Differential Privacy for Heart Disease Prediction: Enhancing Privacy and Accuracy
SN - 978-989-758-737-5
AU - Moulahi W.
AU - Moulahi T.
AU - Jdey I.
AU - Zidi S.
PY - 2025
SP - 845
EP - 852
DO - 10.5220/0013188900003890
PB - SciTePress