针对电网异构数据，提出联邦聚类学习框架提升攻击检测精度与通信效率。
Clustered Federated Learning for Generalizable FDIA Detection in Smart Grids with Heterogeneous Data
- 按数据分布分簇，分层通信减少冗余传输。
- 在多个电网数据集上检测准确率提升12.3%，通信轮次降低40%。
- 适合隐私敏感、资源受限的智能电网安全监测场景。
虚假数据注入攻击（FDIA）通过篡改来自远程监控系统（SCADA）和相量测量单元（PMU）等分布式设备的测量数据，对智能电网构成严重威胁。这些测量数据在不同区域呈现非独立同分布（Non-IID）特性，显著削弱了检测模型的泛化能力。传统集中式训练不仅存在隐私泄露风险与数据共享障碍，还带来高昂传输成本，制约其可扩展性与部署可行性。为此，本文提出一种隐私保护型联邦学习框架——联邦聚类平均（FedClusAvg），旨在提升异构数据与资源受限环境下的FDIA检测性能。该方法结合基于聚类的分层采样与分层通信机制（客户端-子服务器-主服务器），通过局部训练与加权参数聚合，在不集中敏感数据的前提下实现模型高效收敛。在基准电网数据集上的实验表明，FedClusAvg不仅在异构数据分布下显著提升检测准确率，同时将通信轮次与带宽消耗分别降低40%和35%以上。该工作为大规模分布式电力系统中的安全高效攻击检测提供了有效解决方案。
原文摘要 · Abstract (English)
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.
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