Privacy-Preserving Household Characteristic Identification With Federated Learning Method

Lin, J; Ma, J; Zhu, J

Privacy-Preserving Household Characteristic Identification With Federated Learning Method

Lin, J Ma, J Zhu, J

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Smart Grid, 2022, 13, (2), pp. 1088-1099
Issue Date:: 2022-03-01

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Field	Value	Language
dc.contributor.author	Lin, J
dc.contributor.author	Ma, J
dc.contributor.author	Zhu, J https://orcid.org/0000-0002-9763-4047
dc.date.accessioned	2023-04-06T01:51:22Z
dc.date.available	2023-04-06T01:51:22Z
dc.date.issued	2022-03-01
dc.identifier.citation	IEEE Transactions on Smart Grid, 2022, 13, (2), pp. 1088-1099
dc.identifier.issn	1949-3053
dc.identifier.issn	1949-3061
dc.identifier.uri	http://hdl.handle.net/10453/169259
dc.description.abstract	Understanding residential household characteristics is crucial for retailers to provide customers personalized services. Current methods infer household characteristics from smart meter data in a centralized manner that requires the data of all retailers to be gathered together for model training. This may raise privacy concerns since the privacy-sensitive data are owned by different retailers, and they may be unwilling to share the raw data. This paper proposes a federated learning (FL) based deep learning model to identify household characteristics. A hybrid model combining the convolutional neural network and long short-term neural network is designed to learn spatial-temporal features from load profiles. It is implemented in a decentralized manner based on the FL framework. To improve the training speed and accuracy, an asynchronous stochastic gradient descent with delay compensation method is proposed to update the global model parameters. Comprehensive experiments are conducted to verify the effectiveness of the proposed method.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Smart Grid
dc.relation.isbasedon	10.1109/TSG.2021.3125677
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering, 0915 Interdisciplinary Engineering
dc.title	Privacy-Preserving Household Characteristic Identification With Federated Learning Method
dc.type	Journal Article
utslib.citation.volume	13
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0915 Interdisciplinary Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-06T01:51:21Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	13
utslib.citation.issue	2

Abstract:

Understanding residential household characteristics is crucial for retailers to provide customers personalized services. Current methods infer household characteristics from smart meter data in a centralized manner that requires the data of all retailers to be gathered together for model training. This may raise privacy concerns since the privacy-sensitive data are owned by different retailers, and they may be unwilling to share the raw data. This paper proposes a federated learning (FL) based deep learning model to identify household characteristics. A hybrid model combining the convolutional neural network and long short-term neural network is designed to learn spatial-temporal features from load profiles. It is implemented in a decentralized manner based on the FL framework. To improve the training speed and accuracy, an asynchronous stochastic gradient descent with delay compensation method is proposed to update the global model parameters. Comprehensive experiments are conducted to verify the effectiveness of the proposed method.

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http://hdl.handle.net/10453/169259