Federated Learning and Graph Neural Networks for Transmission System Stability: A Privacy-Preserving Artificial Intelligence Framework for Smart Grids

Abstract

Integrating renewables and grid-edge technologies has increased modern power systems’ complexity and data intensity, necessitating advanced Artificial Intelligence (AI) for stability management. However, centralized AI faced fundamental limitations due to data privacy, sovereignty, and communication constraints. This review explored the fusion of Federated Learning (FL) and Graph Neural Networks (GNNs) as a transformative privacy-preserving paradigm. FL enabled collaborative training across decentralized utilities without raw data sharing, while GNNs natively modelled grid topology. The study systematically analyzed FL–GNN applications in state estimation, stability assessment, anomaly detection, and resilient control. Key challenges—including data heterogeneity, communication efficiency, and privacy–accuracy trade-offs—were critically examined. The review concluded by outlining future pathways, such as physics-informed models and digital twin integration, highlighting the potential of FL and GNNs for secure and intelligent grid management. Reported implementations show up to 8% higher predictive accuracy and over 10% improvement in real-time control efficiency compared to centralized models.