Federated anomaly detection in smart grid systems

Abstract

Smart Grid infrastructures have enhanced energy distribution efficiency, reliability, and sustainability, but their proper operation requires robust anomaly detection to mitigate risks from equipment failures, cyberattacks, and natural disasters. Federated Learning (FL) offers a privacy-preserving solution by allowing power plants and grid sectors to collaboratively train models without sharing raw data, addressing privacy concerns, regulatory compliance, and single points of failure that often emerge in centralized approaches. FL also improves real-time anomaly detection and scalability by adapting dynamically to different grid topologies while incurring minimal communication overhead. Within our FL framework, Transformer models excel in anomaly detection due to their self-attention mechanisms that capture intricate temporal dependencies in sensor data. Unlike traditional models, Transformers effectively learn long-range patterns, enhancing detection accuracy and responsiveness. This work conducts a comparative study of two state-of-the-art Tranformer models in an FL environment, evaluating their anomaly detection performance across four diverse smart grid datasets. To assess robustness, we introduce a GAN-based Anomaly Injection Attack (GAIA) that generates and injects realistic syntheitc anomalies. Our results indicate that both federated Transformer models achieve high detection performance across seven metrics, even under adversarial conditions, offering valuable insights into their capabilities in decentralized smart grid applications.