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On the Performance Impact of Poisoning Attacks on Load Forecasting in Federated Learning

Authors:

Naik Bakht Sania Qureshi,

Dong-Hoon Kim,

Jiwoo Lee,

Eun-Kyu LeeAuthors Info & Claims

UbiComp/ISWC '21 Adjunct: Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers

Pages 64 - 66

https://doi.org/10.1145/3460418.3479285

Published: 24 September 2021 Publication History

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Abstract

This article examines a poisoning attack on federated learning. While recent studies are actively exploring this topic in classification models of learning such as image recognition, there are few studies that address the topic in regression models. In particular, this research investigates the impacts of poisoning attacks on the performance of load forecasting, which has hardly studied yet in academia. This research implements two poisoning attacks on a federated learning setting and runs experiments to enumerate their impacts on prediction accuracy of load forecasting. With initial results, we plan to bring a couple of research questions for open discussion to audience.

References

[1]

PJM.Com. [n.d.]. PJM - Data Miner 2, Hourly Load Data. Retrieved June 9, 2021 from https://dataminer2.pjm.com/feed/hrl_load_metered

Google Scholar

[2]

Elif Ustundag Soykan, Zeki Bilgin, Mehmet Akif Ersoy, and Emrah Tomur. 2019. Differentially Private Deep Learning for Load Forecasting on Smart Grid. In Proceedings of the IEEE Globecom Workshops (GC Wkshps). IEEE, 1–6.

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Afaf Taïk and Soumaya Cherkaoui. 2020. Electrical Load Forecasting Using Edge Computing and Federated Learning. In Proceedings of the IEEE International Conference on Communications (ICC). IEEE, 1–6.

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Yan PWang HSong THua YMa RHu NHaghighat MGuan H(2024)SKYMASK: Attack-Agnostic Robust Federated Learning with Fine-Grained Learnable MasksComputer Vision – ECCV 202410.1007/978-3-031-72655-2_17(291-308)Online publication date: 6-Dec-2024
https://doi.org/10.1007/978-3-031-72655-2_17
Sharma GChamikara MChhetri MChen Y(2023)SoK: Systematizing Attack Studies in Federated Learning – From Sparseness to CompletenessProceedings of the ACM Asia Conference on Computer and Communications Security10.1145/3579856.3590328(579-592)Online publication date: 10-Jul-2023
https://doi.org/10.1145/3579856.3590328
Li HWu SWang RGuo YLi J(2023)Fed‐SAD: A secure aggregation federated learning method for distributed short‐term load forecastingIET Generation, Transmission & Distribution10.1049/gtd2.1302217:22(5090-5100)Online publication date: 15-Oct-2023
https://doi.org/10.1049/gtd2.13022
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Information

Published In

September 2021

711 pages

ISBN:9781450384612

DOI:10.1145/3460418

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 24 September 2021

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UbiComp '21

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UbiComp '21: The 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 21 - 26, 2021

Virtual, USA

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Yan PWang HSong THua YMa RHu NHaghighat MGuan H(2024)SKYMASK: Attack-Agnostic Robust Federated Learning with Fine-Grained Learnable MasksComputer Vision – ECCV 202410.1007/978-3-031-72655-2_17(291-308)Online publication date: 6-Dec-2024
https://doi.org/10.1007/978-3-031-72655-2_17
Sharma GChamikara MChhetri MChen Y(2023)SoK: Systematizing Attack Studies in Federated Learning – From Sparseness to CompletenessProceedings of the ACM Asia Conference on Computer and Communications Security10.1145/3579856.3590328(579-592)Online publication date: 10-Jul-2023
https://doi.org/10.1145/3579856.3590328
Li HWu SWang RGuo YLi J(2023)Fed‐SAD: A secure aggregation federated learning method for distributed short‐term load forecastingIET Generation, Transmission & Distribution10.1049/gtd2.1302217:22(5090-5100)Online publication date: 15-Oct-2023
https://doi.org/10.1049/gtd2.13022
Huang CChen WWang XHong FYang SChen YBu SJiang CZhou YZhang Y(2022)DearFSAC: A DRL-based Robust Design for Power Demand Forecasting in Federated Smart GridGLOBECOM 2022 - 2022 IEEE Global Communications Conference10.1109/GLOBECOM48099.2022.10001127(5279-5284)Online publication date: 4-Dec-2022
https://doi.org/10.1109/GLOBECOM48099.2022.10001127

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A Comprehensive Survey on Poisoning Attacks and Countermeasures in Machine Learning

Two-phase Defense Against Poisoning Attacks on Federated Learning-based Intrusion Detection

Dynamic defense against byzantine poisoning attacks in federated learning

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