Clustered Federated Learning for Generalizable FDIA Detection in Smart Grids with Heterogeneous Data

2025/07/20 by Yunfeng Li, Li, Yunfeng, Junhong Liu +6
Computer Science · Engineering · #FOS: Computer and information sciences #FOS: Electrical engineering #Internet Traffic Analysis and Secure E-voting #Machine Learning (cs.LG) #Network Security and Intrusion Detection #Smart Grid Security and Resilience #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2507.14999

openalex publication_date 2025/07/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

False Data Injection Attacks (FDIAs) pose severe security risks to smart grids by manipulating measurement data collected from spatially distributed devices such as SCADA systems and PMUs. These measurements typically exhibit Non-Independent and Identically Distributed (Non-IID) characteristics across different regions, which significantly challenges the generalization ability of detection models. Traditional centralized training approaches not only face privacy risks and data sharing constraints but also incur high transmission costs, limiting their scalability and deployment feasibility. To address these issues, this paper proposes a privacy-preserving federated learning framework, termed Federated Cluster Average (FedClusAvg), designed to improve FDIA detection in Non-IID and resource-constrained environments. FedClusAvg incorporates cluster-based stratified sampling and hierarchical communication (client-subserver-server) to enhance model generalization and reduce communication overhead. By enabling localized training and weighted parameter aggregation, the algorithm achieves accurate model convergence without centralizing sensitive data. Experimental results on benchmark smart grid datasets demonstrate that FedClusAvg not only improves detection accuracy under heterogeneous data distributions but also significantly reduces communication rounds and bandwidth consumption. This work provides an effective solution for secure and efficient FDIA detection in large-scale distributed power systems.

Citations

• Rec-AD: An Efficient Computation Framework for FDIA Detection Based on Tensor Train Decomposition and Deep Learning Recommendation Model
• Byzantine-Resilient Distributed P2P Energy Trading via Spatial-Temporal Anomaly Detection
• Federated Learning for Cyber Physical Systems: A Comprehensive Survey
• Federated Learning for Smart Grid: A Survey on Applications and Potential Vulnerabilities
• Privacy-Preserving Peer-to-Peer Energy Trading via Hybrid Secure Computations
• FedWOA: A Federated Learning Model that uses the Whale Optimization Algorithm for Renewable Energy Prediction
• Federated Learning Based Distributed Localization of False Data Injection Attacks on Smart Grids
• A Federated Learning Approach to Anomaly Detection in Smart Buildings
• Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
• Federated Learning: Challenges, Methods, and Future Directions
• Federated Optimization in Heterogeneous Networks

Related

• Federated Learning Based Distributed Localization of False Data Injection Attacks on Smart Grids
• Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip Approach
• Federated Learning for Smart Grid: A Survey on Applications and Potential Vulnerabilities
• FeDiSa: A Semi-asynchronous Federated Learning Framework for Power System Fault and Cyberattack Discrimination
• FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination