ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
This article is part of the Research TopicAI-Enabled Secure, Resilient, and Autonomous Smart Environments: From Intelligent Cities to Next-Gen GridsView all articles
Federated Learning for Critical Electrical Infrastructure - Handling Data Heterogeneity for Predictive Maintenance of Substation Equipment
Provisionally accepted
- Black & Veatch, Kansas City, United States
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
High-voltage substations form the backbone of critical electrical infrastructure, making predictive maintenance essential for ensuring grid resilience and operational reliability. Federated learning (FL) presents an innovative strategy for predictive maintenance, allowing multiple utility providers to improve model performance jointly while maintaining data confidentiality. Rather than transmitting raw records, each electrical utility performs local model updates and shares only the refined parameters, thereby safeguarding sensitive information and capitalizing on the heterogeneity of equipment conditions across sites. This study develops a set of privacy-preserving FL frameworks to enhance preventive maintenance of substation circuit breakers, large power transformers, and emergency generators. It rigorously tackles the issue of data heterogeneity arising from variations in distribution patterns across utilities, an inherent challenge that hampers effective collaborative model development. Four FL strategies - Federated Averaging (FedAvg and FedAvgM), Federated Proximal (FedProx), and Federated Batch Normalization (FedBN), are evaluated for robustness against distributional shifts. Model performance in this study is evaluated using the F-score, which for the non-IID case ranges from 0.60 to 0.88 depending on the number of clients, the federated learning algorithm used, and the non-IID partitioning strategy employed. Also, a first-of-a kind Federated Information Criterion (FIC) is proposed in this manuscript as an extension of the classical information criterion. The results demonstrate that FedBN is best suited in mitigating cross-utility heterogeneity, yielding highest F-score of 0.88 and a moderately low FIC score of 4.35. Such tailored FL methods significantly improve predictive accuracy, enabling scalable and privacy-preserving deployment of FL in critical power system applications.
Keywords: distributedlearning, Federated Information Criterion, Federated learning, preventive maintenance, Substation maintenance
Received: 01 Sep 2025; Accepted: 10 Dec 2025.
Copyright: © 2025 Ghosh and Mittal. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Soham Ghosh
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.