Sudars, K., Namatevs, I., Nikulins, A., & Ozols, K.. Privacy Auditing of Lithium-Ion Battery Ageing Model by Recovering Time-Series Data Using Gradient Inversion Attack in Federated Learning. Applied Sciences, 15(10), 5704 pp. MDPI, 2025.
Bibtex citation:
Bibtex citation:
@article{18023_2025,
author = {Sudars and K. and Namatevs and I. and Nikulins and A. and & Ozols and K.},
title = {Privacy Auditing of Lithium-Ion Battery Ageing Model by Recovering Time-Series Data Using Gradient Inversion Attack in Federated Learning},
journal = {Applied Sciences},
volume = {15},
issue = {10},
pages = {5704},
publisher = {MDPI},
year = {2025}
}
author = {Sudars and K. and Namatevs and I. and Nikulins and A. and & Ozols and K.},
title = {Privacy Auditing of Lithium-Ion Battery Ageing Model by Recovering Time-Series Data Using Gradient Inversion Attack in Federated Learning},
journal = {Applied Sciences},
volume = {15},
issue = {10},
pages = {5704},
publisher = {MDPI},
year = {2025}
}
Abstract: The exchange of gradients is a widely used method in modelling systems for machine learning (e.g., distributed training, federated learning) in privacy-sensitive domains. Unfortunately, there are still privacy risks in federated learning, as servers can reconstruct clients‘ training data through gradient inversion attacks. To protect against such attacks, we need to know when and how privacy is being undermined, largely due to the black box nature of deep neural networks. Although gradient inversion has been used to classify images and text data, its use in time-series is still largely unexplored. In this paper, we empirically evaluate the practicality of recovering lithium-ion battery time-series data from the gradients of a transformer-based model, both without and with differential privacy, in a time-series federated learning framework. It is especially significant in the case of electric vehicles (EVs). As shown in this paper, additional protection by differential privacy leads to the saturation of gradient inversion attacks, i.e., the reconstructed signal maintains a certain error level, depending on the applied privacy budget level. With this empirical evaluation, we provide insights into effective gradient perturbation directions, unfairness with respect to privacy, and privacy-preferred model initialisation.
Quartile: Q1