SecFedSA: A Secure Differential-Privacy-Based Federated Learning Approach for Smart Cyber-Physical Grid Stability Assessment

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

Abstract: Enhanced by machine learning (ML) techniques, data-driven stability assessment (SA) in smart cyber–physical grids has attracted significant research interest in recent years. However, the current centralized ML architectures have limited scalability, are vulnerable to privacy exposure, and are costly to manage. To resolve these limitations, we propose a novel secure distributed SA method based on federated learning (FL) and differential privacy (DP), namely, Secure Federated SA (SecFedSA). It leverages local system operating data to predict and estimate the system stability status and optimize the power systems in a decentralized fashion. In order to preserve the privacy of the distributed SA operating data, SecFedSA incorporates Gaussian mechanism into DP. Theoretical analysis on the Gaussian mechanism of SecFedSA provides formal DP guarantees. Extensive experiments conducted on the New England 10-machine 39-bus testing system and the synthetic Illinois 49-machine 200-bus testing system demonstrate that the proposed SecFedSA method can achieve advantageous SA performance, while protecting the privacy of the local model information compared to the state of the art.