The increasing security and privacy concerns in the Smart Grid sector have led to a significant demand for robust intrusion detection systems within critical smart grid infrastructure. To address the challenges posed by privacy preservation and decentralized power system zones with distinct data ownership, Federated Learning (FL) has emerged as a promising privacy-preserving solution which facilitates collaborative training of attack detection models without necessitating the sharing of raw data. However, FL presents several implementation limitations in the power system domain due to its heavy reliance on a centralized aggregator and the risks of privacy leakage during model update transmission. To overcome these technical bottlenecks, this paper introduces a novel decentralized federated anomaly detection scheme based on two main gossip protocols namely Random Walk and Epidemic. Our findings indicate that the Random Walk protocol exhibits superior performance compared to the Epidemic protocol, highlighting its efficacy in decentralized federated learning environments. Experimental validation of the proposed framework utilizing publicly available industrial control systems datasets demonstrates superior attack detection accuracy while safeguarding data confidentiality and mitigating the impact of communication latency and stragglers. Furthermore, our approach yields a notable 35% improvement in training time compared to conventional FL, underscoring the efficacy and robustness of our decentralized learning method.

翻译：智能电网领域日益增长的安全与隐私问题，对关键智能电网基础设施中鲁棒的入侵检测系统提出了重大需求。为应对隐私保护以及具有不同数据所有权的去中心化电力系统区域所带来的挑战，联邦学习作为一种有前景的隐私保护解决方案应运而生，它能在无需共享原始数据的情况下，促进攻击检测模型的协同训练。然而，由于联邦学习严重依赖中心化聚合器，且在模型更新传输过程中存在隐私泄露风险，其在电力系统领域的应用面临若干实施限制。为克服这些技术瓶颈，本文提出了一种新颖的去中心化联邦异常检测方案，该方案基于两种主要的Gossip协议，即随机游走和流行病协议。我们的研究结果表明，随机游走协议相比流行病协议表现出更优越的性能，突显了其在去中心化联邦学习环境中的有效性。利用公开可用的工业控制系统数据集对所提框架进行的实验验证表明，该框架在保护数据机密性、减轻通信延迟和掉队者影响的同时，实现了卓越的攻击检测准确率。此外，与传统联邦学习方法相比，我们的方法在训练时间上实现了35%的显著提升，这证明了我们提出的去中心化学习方法的有效性和鲁棒性。