  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.
