QFDSA: A Quantum-Secured Federated Learning System for Smart Grid Dynamic Security Assessment

Chao Ren, Rudai Yan, Minrui Xu, Han Yu, Yan Xu, Dusit Niyato, Zhao Yang Dong

Abstract: Enhanced by machine learning (ML) techniques, data-driven dynamic security assessment (DSA) in smart cyber-physical grids has attracted great research interests in recent years. However, as existing DSA methods generally rely on centralized ML architectures, the scalability, privacy, and cost effectiveness of existing methods are limited. To address these issues, we propose a novel quantum-secured distributed intelligent system for smart cyber-physical DSA based on Federated learning (FL) and quantum key distribution (QKD), namely, quantum-secured federated DSA (QFDSA). QFDSA aggregates the knowledge learned from various local data owners (also known as clients) to predict and evaluate the system stability status in a decentralized fashion. In addition, in order to preserve the privacy of the distributed DSA data, QFDSA adopts the measurement-device-independent QKD, which can further improve the security of local DSA model transmission. Moreover, to accommodate the typical fast system environment and requirement changes, QFDSA alleviates the issues of limited key generation rates by utilizing secret-key pool that guarantee the availability of adequate secret-key materials. Extensive experiments based on the New England 10-machine 39-bus testing system and the synthetic Illinois 49-machine 200-bus testing system demonstrate that the proposed QFDSA method can achieve more advantageous DSA performance while protecting the privacy of local data for real-time DSA applications compared to the benchmarks. Besides, the secret-key generation rate can be improved to adjust its parameters dynamically in real time.