Federated Learning: A Paradigm Shift in Cybersecurity for Smart Grids

The increasing complexity and connectivity of smart distribution grids necessitate advanced cybersecurity measures to protect against sophisticated threats. This paper presents a novel cybersecurity framework utilizing Federated Learning (FL) to enhance the resilience of smart distribution grids against cyber-attacks. Unlike traditional centralized learning approaches, FL enables a collaborative yet privacy-preserving data analysis across multiple grid nodes without the need to share sensitive information which could be intercepted and compromise the whole smart distribution grid. In this paper we present a decentralized model that leverages distributed data sources to detect and respond to anomalous behaviors effectively. The framework is designed to adapt to new threats dynamically, thereby improving its robustness over time. This framework shows the potential of FL in fostering a secure and resilient infrastructure for smart grids, paving the way for its adoption in critical energy systems. This study also discusses the challenges of implementing FL in a real-world environment, including scalability and coordination among disparate grid elements. Finally, recommendations for future research directions are provided, emphasizing enhancements in algorithm efficiency and cross-sectoral adaptability.