Privacy-Preserving Distributed Learning for Residential Short-Term Load Forecasting

ArXiv Pub Date : 2024-02-02 DOI:10.1109/jiot.2024.3362587
Yizhen Dong, Yingjie Wang, Mariana Gama, Mustafa A. Mustafa, G. Deconinck, Xiaowei Huang
{"title":"Privacy-Preserving Distributed Learning for Residential Short-Term Load Forecasting","authors":"Yizhen Dong, Yingjie Wang, Mariana Gama, Mustafa A. Mustafa, G. Deconinck, Xiaowei Huang","doi":"10.1109/jiot.2024.3362587","DOIUrl":null,"url":null,"abstract":"In the realm of power systems, the increasing involvement of residential users in load forecasting applications has heightened concerns about data privacy. Specifically, the load data can inadvertently reveal the daily routines of residential users, thereby posing a risk to their property security. While federated learning (FL) has been employed to safeguard user privacy by enabling model training without the exchange of raw data, these FL models have shown vulnerabilities to emerging attack techniques, such as Deep Leakage from Gradients and poisoning attacks. To counteract these, we initially employ a Secure-Aggregation (SecAgg) algorithm that leverages multiparty computation cryptographic techniques to mitigate the risk of gradient leakage. However, the introduction of SecAgg necessitates the deployment of additional sub-center servers for executing the multiparty computation protocol, thereby escalating computational complexity and reducing system robustness, especially in scenarios where one or more sub-centers are unavailable. To address these challenges, we introduce a Markovian Switching-based distributed training framework, the convergence of which is substantiated through rigorous theoretical analysis. The Distributed Markovian Switching (DMS) topology shows strong robustness towards the poisoning attacks as well. Case studies employing real-world power system load data validate the efficacy of our proposed algorithm. It not only significantly minimizes communication complexity but also maintains accuracy levels comparable to traditional FL methods, thereby enhancing the scalability of our load forecasting algorithm.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":"299 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ArXiv","FirstCategoryId":"0","ListUrlMain":"https://doi.org/10.1109/jiot.2024.3362587","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In the realm of power systems, the increasing involvement of residential users in load forecasting applications has heightened concerns about data privacy. Specifically, the load data can inadvertently reveal the daily routines of residential users, thereby posing a risk to their property security. While federated learning (FL) has been employed to safeguard user privacy by enabling model training without the exchange of raw data, these FL models have shown vulnerabilities to emerging attack techniques, such as Deep Leakage from Gradients and poisoning attacks. To counteract these, we initially employ a Secure-Aggregation (SecAgg) algorithm that leverages multiparty computation cryptographic techniques to mitigate the risk of gradient leakage. However, the introduction of SecAgg necessitates the deployment of additional sub-center servers for executing the multiparty computation protocol, thereby escalating computational complexity and reducing system robustness, especially in scenarios where one or more sub-centers are unavailable. To address these challenges, we introduce a Markovian Switching-based distributed training framework, the convergence of which is substantiated through rigorous theoretical analysis. The Distributed Markovian Switching (DMS) topology shows strong robustness towards the poisoning attacks as well. Case studies employing real-world power system load data validate the efficacy of our proposed algorithm. It not only significantly minimizes communication complexity but also maintains accuracy levels comparable to traditional FL methods, thereby enhancing the scalability of our load forecasting algorithm.
针对住宅短期负荷预测的隐私保护分布式学习
在电力系统领域,越来越多的居民用户参与到负荷预测应用中,这加剧了人们对数据隐私的担忧。具体来说,负荷数据可能会无意中泄露居民用户的日常作息时间,从而对他们的财产安全构成威胁。虽然联合学习(FL)通过在不交换原始数据的情况下进行模型训练来保护用户隐私,但这些联合学习模型在新出现的攻击技术(如梯度深度泄漏和中毒攻击)面前表现出脆弱性。为了应对这些问题,我们最初采用了安全聚合(SecAgg)算法,利用多方计算加密技术来降低梯度泄漏的风险。然而,SecAgg 的引入需要部署额外的分中心服务器来执行多方计算协议,从而增加了计算复杂性,降低了系统的鲁棒性,尤其是在一个或多个分中心不可用的情况下。为了应对这些挑战,我们引入了基于马尔可夫交换的分布式训练框架,并通过严格的理论分析证实了该框架的收敛性。分布式马尔可夫交换(DMS)拓扑对中毒攻击也表现出很强的鲁棒性。利用真实电力系统负荷数据进行的案例研究验证了我们提出的算法的有效性。它不仅大大降低了通信复杂度,而且保持了与传统 FL 方法相当的准确度水平,从而增强了我们的负荷预测算法的可扩展性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信