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User privacy leakages from federated learning in NILM applications

Authors:

Yunchuan Shi,

Wei Li,

Xiaomin Chang,

Albert Y. ZomayaAuthors Info & Claims

BuildSys '21: Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

Pages 212 - 213

https://doi.org/10.1145/3486611.3492222

Published: 17 November 2021 Publication History

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Abstract

Non-intrusive load monitoring (NILM) is a technology that estimates the energy consumed by each appliance in the building from the main electricity meter reading only. Federal Learning (FL) is increasingly employed to construct a distributed learning environment to address the lack of data issues in NILM applications. Although FL inherently provides client privacy by sharing training parameters instead of raw data with the federated server, this does not ensure the user privacy is in absolute security. This work aims to investigate the potential user privacy leakage issues of NILM applications using the federated learning frameworks. We experimentally study what data can be revealed and how vulnerable they can be. We are also towards building a new federated learning framework to provide better security for NILM applications.

References

[1]

R Gopinath, Mukesh Kumar, and Kota Srinivas. 2020. Feature Mapping based Deep Neural Networks for Non-intrusive Load Monitoring of Similar Appliances in Buildings. In Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. 262--265.

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[2]

Rithwik Kukunuri, Anup Aglawe, Jainish Chauhan, Kratika Bhagtani, Rohan Patil, Sumit Walia, and Nipun Batra. 2020. EdgeNFLM: towards NILM on edge devices. In Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. 90--99.

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Changho Shin, Sunghwan Joo, Jaeryun Yim, Hyoseop Lee, Taesup Moon, and Wonjong Rhee. 2019. Subtask gated networks for non-intrusive load monitoring. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 1150--1157.

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Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen. 2020. idlg: Improved deep leakage from gradients. arXiv preprint arXiv:2001.02610 (2020).

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Cited By

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Bousbiat HHimeur YVarlamis IBensaali FAmira A(2023)Neural Load Disaggregation: Meta-Analysis, Federated Learning and BeyondEnergies10.3390/en1602099116:2(991)Online publication date: 16-Jan-2023
https://doi.org/10.3390/en16020991
Shi YLi WChang XYang TSun YZomaya A(2023)On enabling collaborative non-intrusive load monitoring for sustainable smart citiesScientific Reports10.1038/s41598-023-33131-013:1Online publication date: 21-Apr-2023
https://doi.org/10.1038/s41598-023-33131-0
Dos Santos LRamos RGomes RLins PDos Santos A(2022)Middleware for Smart Campus applications based in Federated LearningProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3539637.3558046(324-328)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3539637.3558046
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User privacy leakages from federated learning in NILM applications

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Information

Published In

BuildSys '21: Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

November 2021

388 pages

ISBN:9781450391146

DOI:10.1145/3486611

General Chair:
Xiaofan (Fred) Jiang
Columbia University
,
Program Chairs:
Omprakash Gnawali
University of Houston
,
Zoltan Nagy
The University of Texas Austin

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.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 November 2021

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Poster

Conference

BuildSys '21

Sponsor:

SIGEnergy

BuildSys '21: The 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

November 17 - 18, 2021

Coimbra, Portugal

Acceptance Rates

BuildSys '21 Paper Acceptance Rate 28 of 107 submissions, 26%;

Overall Acceptance Rate 148 of 500 submissions, 30%

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Cited By

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Bousbiat HHimeur YVarlamis IBensaali FAmira A(2023)Neural Load Disaggregation: Meta-Analysis, Federated Learning and BeyondEnergies10.3390/en1602099116:2(991)Online publication date: 16-Jan-2023
https://doi.org/10.3390/en16020991
Shi YLi WChang XYang TSun YZomaya A(2023)On enabling collaborative non-intrusive load monitoring for sustainable smart citiesScientific Reports10.1038/s41598-023-33131-013:1Online publication date: 21-Apr-2023
https://doi.org/10.1038/s41598-023-33131-0
Dos Santos LRamos RGomes RLins PDos Santos A(2022)Middleware for Smart Campus applications based in Federated LearningProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3539637.3558046(324-328)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3539637.3558046
Bousbiat HKlemenjak CHimeur YElmenreich WAmira AMansoor WAtalla S(2022)Neural NILM Learning Paradigms: From Centralised to Decentralised Learning2022 5th International Conference on Signal Processing and Information Security (ICSPIS)10.1109/ICSPIS57063.2022.10002485(138-142)Online publication date: 7-Dec-2022
https://doi.org/10.1109/ICSPIS57063.2022.10002485
Wang XLi W(2022)MTFed-NILM: Multi-Task Federated Learning for Non-Intrusive Load Monitoring2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927999(1-8)Online publication date: 12-Sep-2022
https://doi.org/10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927999

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