Predicting the State of Health of Supercapacitors Using a Federated Learning Model with Homomorphic Encryption

Víctor López, Oscar Fontenla-Romero, Elena Hernández-Pereira, Bertha Guijarro-Berdiñas, Carlos Blanco-Seijo, Samuel Fernández-Paz

2025

Abstract

The increasing prevalence of supercapacitors (SCs) in various industrial sectors underscores the necessity for precise estimation of the state of health (SOH) of these devices. This article presents a novel approach to SOH prediction using a model that integrates federated learning (FL) and homomorphic encryption (HE), FedHEONN. Conventional SOH prediction models face challenges concerning accuracy, reliability, and secure data handling, particularly in Internet of Things (IoT) environments. FedHEONN addresses these issues by using FL to enable a network of distributed nodes to collaboratively develop a predictive model without the need to share private data. This model enhances both data privacy and leverages the collective intelligence of edge computing devices. Furthermore, the inclusion of HE allows computations to be performed on encrypted data, further securing the federated learning framework. We conducted experiments with a real dataset to evaluate the effectiveness of this FL method in predicting the SOH of SCs against conventional models, including linear regression with regularisation techniques such as Lasso, Ridge and Elastic-net, and non-linear models such as multilayer perceptron and support vector machine for regression. The results were tested in various configurations, including empirical mode decomposition (EMD) and multi-stage (MS) setups.

Paper Citation

in Harvard Style

López V., Fontenla-Romero O., Hernández-Pereira E., Guijarro-Berdiñas B., Blanco-Seijo C. and Fernández-Paz S. (2025). Predicting the State of Health of Supercapacitors Using a Federated Learning Model with Homomorphic Encryption. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 884-891. DOI: 10.5220/0013215300003890

in Bibtex Style

@conference{icaart25,
author={Víctor López and Oscar Fontenla-Romero and Elena Hernández-Pereira and Bertha Guijarro-Berdiñas and Carlos Blanco-Seijo and Samuel Fernández-Paz},
title={Predicting the State of Health of Supercapacitors Using a Federated Learning Model with Homomorphic Encryption},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={884-891},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013215300003890},
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 - Predicting the State of Health of Supercapacitors Using a Federated Learning Model with Homomorphic Encryption
SN - 978-989-758-737-5
AU - López V.
AU - Fontenla-Romero O.
AU - Hernández-Pereira E.
AU - Guijarro-Berdiñas B.
AU - Blanco-Seijo C.
AU - Fernández-Paz S.
PY - 2025
SP - 884
EP - 891
DO - 10.5220/0013215300003890
PB - SciTePress