Addressing Heterogeneity in Federated Load Forecasting with Personalization Layers
<|reference_start|>Addressing Heterogeneity in Federated Load Forecasting with Personalization Layers: The advent of smart meters has enabled pervasive collection of energy consumption data for training short-term load forecasting models. In response to privacy concerns, federated learning (FL) has been proposed as a privacy-preserving approach for training, but the quality of trained models degrades as client data becomes heterogeneous. In this paper we propose the use of personalization layers for load forecasting in a general framework called PL-FL. We show that PL-FL outperforms FL and purely local training, while requiring lower communication bandwidth than FL. This is done through extensive simulations on three different datasets from the NREL ComStock repository.<|reference_end|>
@article{bose2024addressing,
    title={Addressing Heterogeneity in Federated Load Forecasting with
  Personalization Layers},
    author={Shourya Bose, Yu Zhang, Kibaek Kim},
    journal={arXiv preprint arXiv:2404.01517},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01517},
    primaryClass={cs.LG eess.SP}
}