Predicting the state of health of supercapacitors using a federated learning model with homomorphic encryption

Abstract

[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.