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

arXiv: https://arxiv.org/abs/2507.14999 · HTML: https://mlsanity.com/paper/2507.14999/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 42 references that are in the map.
- First posted on arXiv 20 Jul 2025.

## Builds on (most cited first, 14 of 14)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach](https://mlsanity.com/paper/2209.00778/) (2022)
- [A Federated Learning Approach to Anomaly Detection in Smart Buildings](https://mlsanity.com/paper/2010.10293/) (2020)
- [Comprehensive Survey and Taxonomies of False Injection Attacks in Smart Grid: Attack Models, Targets, and Impacts](https://mlsanity.com/paper/2103.10594/) (2021)
- [Federated Learning for Cyber Physical Systems: A Comprehensive Survey](https://mlsanity.com/paper/2505.04873/) (2025)
- [Federated Learning for Smart Grid: A Survey on Applications and Potential Vulnerabilities](https://mlsanity.com/paper/2409.10764/) (2024)
- [Federated Learning Based Distributed Localization of False Data Injection Attacks on Smart Grids](https://mlsanity.com/paper/2306.10420/) (2023)
- [FedWOA: A Federated Learning Model that uses the Whale Optimization Algorithm for Renewable Energy Prediction](https://mlsanity.com/paper/2309.10337/) (2023)
- [Byzantine-Resilient Distributed P2P Energy Trading via Spatial-Temporal Anomaly Detection](https://mlsanity.com/paper/2505.20567/) (2025)
- [Privacy-Preserving Peer-to-Peer Energy Trading via Hybrid Secure Computations](https://mlsanity.com/paper/2505.20577/) (2025)
- [Rec-AD: An Efficient Computation Framework for FDIA Detection Based on Tensor Train Decomposition and Deep Learning Recommendation Model](https://mlsanity.com/paper/2507.14668/) (2025)

## Abstract (arXiv)

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.