Addressing Heterogeneity in Federated Load Forecasting with
  Personalization Layers
Shourya Bose Yu Zhang Kibaek Kim
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.