Reference : Privacy-preserving federated learning for residential short-term load forecasting |
Scientific journals : Article | |||
Engineering, computing & technology : Computer science Engineering, computing & technology : Energy Business & economic sciences : Management information systems | |||
Security, Reliability and Trust | |||
http://hdl.handle.net/10993/52125 | |||
Privacy-preserving federated learning for residential short-term load forecasting | |
English | |
[en] Volume 326, 15 November 2022, 119915 | |
Delgado Fernandez, Joaquin ![]() | |
Potenciano Menci, Sergio ![]() | |
Lee, Chul Min [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX >] | |
Rieger, Alexander ![]() | |
Fridgen, Gilbert ![]() | |
15-Nov-2022 | |
Applied Energy | |
Elsevier | |
326 | |
Yes | |
International | |
0306-2619 | |
1872-9118 | |
London | |
United Kingdom | |
[en] Deep neural networks ; Differential privacy ; Federated learning ; Secure aggregation ; Privacy-preserving federated learning ; Short-term load forecasting | |
[en] With high levels of intermittent power generation and dynamic demand patterns, accurate forecasts for residential loads have become essential. Smart meters can play an important role when making these forecasts as they provide detailed load data. However, using smart meter data for load forecasting is challenging due to data privacy requirements. This paper investigates how these requirements can be addressed through a combination of federated learning and privacy preserving techniques such as differential privacy and secure aggregation. For our analysis, we employ a large set of residential load data and simulate how different federated learning models and privacy preserving techniques affect performance and privacy. Our simulations reveal that combining federated learning and privacy preserving techniques can secure both high forecasting accuracy and near-complete privacy. Specifically, we find that such combinations enable a high level of information sharing while ensuring privacy of both the processed load data and forecasting models. Moreover, we identify and discuss challenges of applying federated learning, differential privacy and secure aggregation for residential short-term load forecasting. | |
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Digital Financial Services and Cross-organizational Digital Transformations (FINATRAX) ; University of Luxembourg: High Performance Computing - ULHPC | |
European Commission - EC | |
Medical Device Obligations Taskforce | |
Researchers | |
http://hdl.handle.net/10993/52125 | |
10.1016/j.apenergy.2022.119915 | |
https://www.sciencedirect.com/science/article/pii/S0306261922011722?via%3Dihub | |
H2020 ; 814654 - MDOT - Medical Device Obligations Taskforce | |
FnR ; FNR13342933 > Gilbert Fridgen > DFS > Paypal-fnr Pearl Chair In Digital Financial Services > 01/01/2020 > 31/12/2024 > 2019 |
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