A Privacy-Enhancing Cross-Silo Federated Learning Framework for False Data Injection Attack Detection in Smart Grids Using Multi-Stage Analytical Modeling

Abstract

Smart grids face an escalating threat of False Data Injection Attacks (FDIAs), which compromise system state estimation, distort operational forecasting, and undermine overall grid reliability. Traditional centralized detection methods fail to protect privacy and become infeasible under the growing interconnectivity of modern power systems. This work presents a novel, five-stage analytical framework that integrates privacy-enhancing cross-silo federated learning with attack-resilient modeling to detect and mitigate FDIAs in distributed smart-grid environments. We introduce five original methods: Cross-Silo Privacy Residual Embedding Transform (CS-PRET), Federated Trust-Aware Behavior Manifold Construction (FT-BMC), Stochastic Attack-Surface Uncertainty Field Estimator (SAUFE), Adversarial-Resilient Federated Signature Extractor (AR-FSE), and Federated Adaptive Decision Fusion & Grid Defense Model (FAD-GDM). These methods form a sequential pipeline where the output of each module becomes the input of the next, enabling end-to-end trustworthy, privacy-preserving, and interpretable FDIA detections. The proposed system is validated using multi-silo synthetic and IEEE-standardized smart-grid datasets. Results demonstrate enhanced privacy guarantees, improved robustness against perturbations, and significant increases in detection accuracy and mitigation performance. Empirical outcomes reveal a reduction in detection latency by 37%, enhancement in FDIA recall to 94%, and a 19–23% improvement in predicted grid stability margins. This study contributes a unified analytical model, theoretical foundation, and experimental validation for cross-silo collaborative defense systems in future cyber-physical smart grids. The paper concludes with a discussion of scalability, interpretability, and opportunities for future work, including self-healing grid architectures, autonomous federated learning ecosystems, and quantum-resilient FDIA detections.