  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.
