Privacy-Preserving Load Forecasting via Personalized Model Obfuscation

• URL: http://arxiv.org/abs/2312.00036v1
• Date: Tue, 21 Nov 2023 03:03:10 GMT
• Title: Privacy-Preserving Load Forecasting via Personalized Model Obfuscation
• Authors: Shourya Bose, Yu Zhang, Kibaek Kim
• Abstract summary: This paper addresses the performance challenges of short-term load forecasting models trained with federated learning on heterogeneous data. Our proposed algorithm, Privacy Preserving Federated Learning (PPFL), incorporates personalization layers for localized training at each smart meter.
• Score: 4.420464017266168
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The widespread adoption of smart meters provides access to detailed and localized load consumption data, suitable for training building-level load forecasting models. To mitigate privacy concerns stemming from model-induced data leakage, federated learning (FL) has been proposed. This paper addresses the performance challenges of short-term load forecasting models trained with FL on heterogeneous data, emphasizing privacy preservation through model obfuscation. Our proposed algorithm, Privacy Preserving Federated Learning (PPFL), incorporates personalization layers for localized training at each smart meter. Additionally, we employ a differentially private mechanism to safeguard against data leakage from shared layers. Simulations on the NREL ComStock dataset corroborate the effectiveness of our approach.

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