Chao Ren;Tianjing Wang;Han Yu;Yan Xu;Zhao Yang Dong
{"title":"EFedDSA: An Efficient Differential Privacy-Based Horizontal Federated Learning Approach for Smart Grid Dynamic Security Assessment","authors":"Chao Ren;Tianjing Wang;Han Yu;Yan Xu;Zhao Yang Dong","doi":"10.1109/JETCAS.2023.3293253","DOIUrl":null,"url":null,"abstract":"Enhanced by machine learning (ML) techniques, data-driven dynamic security assessment (DSA) 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 effective and secure distributed DSA method based on horizontal federated learning (HFL) and differential privacy (DP), namely EFedDSA. 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 DSA operating data, EFedDSA incorporates Gaussian mechanism into DP. To reduce the computational burden from multiple transmission communication rounds, a discounting method for the total communication round is proposed to reduce the total transmission rounds. Theoretical analysis on the Gaussian mechanism of EFedDSA 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 EFedDSA method can achieve advantageous DSA performance with fewer communication rounds, while protecting the privacy of the local model information compared to the state of the art.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10183839/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Enhanced by machine learning (ML) techniques, data-driven dynamic security assessment (DSA) 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 effective and secure distributed DSA method based on horizontal federated learning (HFL) and differential privacy (DP), namely EFedDSA. 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 DSA operating data, EFedDSA incorporates Gaussian mechanism into DP. To reduce the computational burden from multiple transmission communication rounds, a discounting method for the total communication round is proposed to reduce the total transmission rounds. Theoretical analysis on the Gaussian mechanism of EFedDSA 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 EFedDSA method can achieve advantageous DSA performance with fewer communication rounds, while protecting the privacy of the local model information compared to the state of the art.
期刊介绍:
The IEEE Journal on Emerging and Selected Topics in Circuits and Systems is published quarterly and solicits, with particular emphasis on emerging areas, special issues on topics that cover the entire scope of the IEEE Circuits and Systems (CAS) Society, namely the theory, analysis, design, tools, and implementation of circuits and systems, spanning their theoretical foundations, applications, and architectures for signal and information processing.