TY - GEN
T1 - DP-AMI-FL
T2 - 2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023
AU - INL Funded (No INL Authors)
AU - Tayeen, Abu Saleh Md
AU - Biswal, Milan
AU - Misra, Satyajayant
AU - Author Other, Hidden
N1 - Funding Information:
ACKNOWLEDGMENT This work was sponsored in part by the DEVCOM Analysis Center and was accomplished under Cooperative Agreement Number W911NF-22-2-0001, the US Department of Energy under grants #DE-AC07-05ID14517, #DE-SC0023392, #DE-EE0008774.
Funding Information:
This work was sponsored in part by the DEVCOM Analysis Center and was accomplished under Cooperative Agreement Number W911NF-22-2-0001, the US Department of Energy under grants #DE-AC07-05ID14517, #DE-SC0023392, #DEEE0008774.
Publisher Copyright:
© 2023 IEEE.
PY - 2023/3/22
Y1 - 2023/3/22
N2 - Machine Learning (ML) algorithms have shown quite promising applications in smart meter data analytics enabling intelligent energy management systems for the Advanced Metering Infrastructure (AMI). One of the major challenges in developing ML applications for the AMI is to preserve user privacy while allowing active end-users participation. This paper addresses this challenge and proposes Differential Privacy-enabled AMI with Federated Learning (DP-AMI-FL), framework for ML-based applications in the AMI. This framework provides two layers of privacy protection: first, it keeps the raw data of consumers hosting ML applications at edge devices (smart meters) with Federated Learning (FL), and second, it obfuscates the ML models using Differential Privacy (DP) to avoid privacy leakage threats on the models posed by various inference attacks. The framework is evaluated by analyzing its performance on a use case aimed to improve Short-Term Load Forecasting (STLF) for residential consumers having smart meters and home energy management systems. Extensive experiments demonstrate that the framework when used with Long Short-Term Memory (LSTM) recurrent neural network models, achieves high forecasting accuracy while preserving users data privacy.
AB - Machine Learning (ML) algorithms have shown quite promising applications in smart meter data analytics enabling intelligent energy management systems for the Advanced Metering Infrastructure (AMI). One of the major challenges in developing ML applications for the AMI is to preserve user privacy while allowing active end-users participation. This paper addresses this challenge and proposes Differential Privacy-enabled AMI with Federated Learning (DP-AMI-FL), framework for ML-based applications in the AMI. This framework provides two layers of privacy protection: first, it keeps the raw data of consumers hosting ML applications at edge devices (smart meters) with Federated Learning (FL), and second, it obfuscates the ML models using Differential Privacy (DP) to avoid privacy leakage threats on the models posed by various inference attacks. The framework is evaluated by analyzing its performance on a use case aimed to improve Short-Term Load Forecasting (STLF) for residential consumers having smart meters and home energy management systems. Extensive experiments demonstrate that the framework when used with Long Short-Term Memory (LSTM) recurrent neural network models, achieves high forecasting accuracy while preserving users data privacy.
KW - advanced metering infrastructure
KW - federated machine learning
KW - short-term load forecasting
KW - Smart meter data analytics
KW - user privacy
UR - https://www.scopus.com/pages/publications/85151498102
UR - https://www.mendeley.com/catalogue/dbc14911-2887-310f-be3b-2d4a46a8d468/
U2 - 10.1109/ISGT51731.2023.10066415
DO - 10.1109/ISGT51731.2023.10066415
M3 - Conference contribution
AN - SCOPUS:85151498102
SN - 9781665453554
T3 - 2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023
SP - 1
EP - 5
BT - 2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 16 January 2023 through 19 January 2023
ER -