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

TL;DR

This paper introduces FedClusAvg, a federated learning framework that enhances FDIA detection in smart grids with heterogeneous data, ensuring privacy, reducing communication costs, and improving model generalization.

Contribution

The paper proposes FedClusAvg, a novel federated clustering approach with hierarchical communication for secure, scalable, and accurate FDIA detection in Non-IID smart grid data.

Findings

Improved detection accuracy under Non-IID data distributions.

Reduced communication rounds and bandwidth usage.

Effective localized training with weighted parameter aggregation.

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.