  With the growing concern about the security and privacy of smart grid
systems, cyberattacks on critical power grid components, such as state
estimation, have proven to be one of the top-priority cyber-related issues and
have received significant attention in recent years. However, cyberattack
detection in smart grids now faces new challenges, including privacy
preservation and decentralized power zones with strategic data owners. To
address these technical bottlenecks, this paper proposes a novel Federated
Learning-based privacy-preserving and communication-efficient attack detection
framework, known as FedDiSC, that enables Discrimination between power System
disturbances and Cyberattacks. Specifically, we first propose a Federated
Learning approach to enable Supervisory Control and Data Acquisition subsystems
of decentralized power grid zones to collaboratively train an attack detection
model without sharing sensitive power related data. Secondly, we put forward a
representation learning-based Deep Auto-Encoder network to accurately detect
power system and cybersecurity anomalies. Lastly, to adapt our proposed
framework to the timeliness of real-world cyberattack detection in SGs, we
leverage the use of a gradient privacy-preserving quantization scheme known as
DP-SIGNSGD to improve its communication efficiency. Extensive simulations of
the proposed framework on publicly available Industrial Control Systems
datasets demonstrate that the proposed framework can achieve superior detection
accuracy while preserving the privacy of sensitive power grid related
information. Furthermore, we find that the gradient quantization scheme
utilized improves communication efficiency by 40% when compared to a
traditional federated learning approach without gradient quantization which
suggests suitability in a real-world scenario.
