Similarity-driven truncated aggregation framework for privacy-preserving short term load forecasting

IF 6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Ahsan Raza Khan, Mohammad Al-Quraan, Lina Mohjazi, David Flynn, Muhammad Ali Imran, Ahmed Zoha
{"title":"Similarity-driven truncated aggregation framework for privacy-preserving short term load forecasting","authors":"Ahsan Raza Khan,&nbsp;Mohammad Al-Quraan,&nbsp;Lina Mohjazi,&nbsp;David Flynn,&nbsp;Muhammad Ali Imran,&nbsp;Ahmed Zoha","doi":"10.1016/j.iot.2025.101530","DOIUrl":null,"url":null,"abstract":"<div><div>Accurate short-term load forecasting (STLF) is essential for the efficient and reliable operation of power systems, enabling effective scheduling and integration of renewable energy sources. Federated learning (FL) offers a collaborative, privacy-preserving approach for distributed model training by avoiding data sharing among sources. However, existing FL methods for STLF often rely on clustering techniques for highly variable residential data, which struggle to effectively handle data diversity, privacy constraints, and anomalous model updates. This study addresses these concerns and presents a similarity-driven truncated aggregation (SDTA) algorithm designed for STLF at macro-level sub-stations. SDTA enhances model alignment by computing layer-wise cosine similarity among client updates and mitigates outliers through truncated mean aggregation, reducing overfitting and improving robustness. The algorithm integrates differential privacy (DP) mechanisms to protect model updates and applies cosine-similarity-based filtering to safeguard against adversarial attacks. Extensive simulations on real-world substation data validate that SDTA significantly outperforms standard FL algorithms such as federated averaging (FedAVG) and federated distance (FedDist). Under conditions without privacy constraints, SDTA achieves a mean absolute percentage error (MAPE) of 2.63%, surpassing FedAVG and FedDist with MAPE of 2.89% and 3.11%, respectively, with faster convergence. Under strict DP constraints, SDTA maintains high forecasting performance with a MAPE of 4.02%, outperforming FedDist and FedAVG by 9.7% and 20.4%, respectively. Furthermore, SDTA exhibits substantial resilience under adversarial conditions, achieving a MAPE reduction of 20.5% over FedAVG when 40% of edge nodes are compromised. Moreover, the study examines the robustness of SDTA against random client selection scenarios, illustrating its resilience and practical applicability in real-world settings, particularly when client selection rates are below 60%.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101530"},"PeriodicalIF":6.0000,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Internet of Things","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2542660525000435","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

Accurate short-term load forecasting (STLF) is essential for the efficient and reliable operation of power systems, enabling effective scheduling and integration of renewable energy sources. Federated learning (FL) offers a collaborative, privacy-preserving approach for distributed model training by avoiding data sharing among sources. However, existing FL methods for STLF often rely on clustering techniques for highly variable residential data, which struggle to effectively handle data diversity, privacy constraints, and anomalous model updates. This study addresses these concerns and presents a similarity-driven truncated aggregation (SDTA) algorithm designed for STLF at macro-level sub-stations. SDTA enhances model alignment by computing layer-wise cosine similarity among client updates and mitigates outliers through truncated mean aggregation, reducing overfitting and improving robustness. The algorithm integrates differential privacy (DP) mechanisms to protect model updates and applies cosine-similarity-based filtering to safeguard against adversarial attacks. Extensive simulations on real-world substation data validate that SDTA significantly outperforms standard FL algorithms such as federated averaging (FedAVG) and federated distance (FedDist). Under conditions without privacy constraints, SDTA achieves a mean absolute percentage error (MAPE) of 2.63%, surpassing FedAVG and FedDist with MAPE of 2.89% and 3.11%, respectively, with faster convergence. Under strict DP constraints, SDTA maintains high forecasting performance with a MAPE of 4.02%, outperforming FedDist and FedAVG by 9.7% and 20.4%, respectively. Furthermore, SDTA exhibits substantial resilience under adversarial conditions, achieving a MAPE reduction of 20.5% over FedAVG when 40% of edge nodes are compromised. Moreover, the study examines the robustness of SDTA against random client selection scenarios, illustrating its resilience and practical applicability in real-world settings, particularly when client selection rates are below 60%.
求助全文
约1分钟内获得全文 求助全文
来源期刊
Internet of Things
Internet of Things Multiple-
CiteScore
3.60
自引率
5.10%
发文量
115
审稿时长
37 days
期刊介绍: Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT. The journal will place a high priority on timely publication, and provide a home for high quality. Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.
×
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:481959085
Book学术官方微信