Addressing Heterogeneity in Federated Load Forecasting with Personalization Layers
This addresses privacy-preserving load forecasting for smart grid applications, but it is incremental as it builds on existing FL methods with a specific adaptation.
The paper tackles the problem of model degradation due to data heterogeneity in federated learning for load forecasting by proposing PL-FL with personalization layers, showing it outperforms FL and local training while reducing communication bandwidth in simulations on NREL datasets.
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.