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The widespread adoption of smart meters provides access to detailed andlocalized load consumption data, suitable for training building-level loadforecasting models. To mitigate privacy concerns stemming from model-induceddata leakage, federated learning (FL) has been proposed. This paper addressesthe performance challenges of short-term load forecasting models trained withFL on heterogeneous data, emphasizing privacy preservation through modelobfuscation. Our proposed algorithm, Privacy Preserving Federated Learning(PPFL), incorporates personalization layers for localized training at eachsmart meter. Additionally, we employ a differentially private mechanism tosafeguard against data leakage from shared layers. Simulations on the NRELComStock dataset corroborate the effectiveness of our approach.

Privacy-Preserving Load Forecasting via Personalized Model Obfuscation