Privacy-Preserving Electricity Load Forecasting using Hierarchical Federated Learning

Smart meters, utilizing their large-scale distributed deployment architecture, facilitate real-time acquisition of fine-grained residential load data, which significantly enhances the accuracy of Short-term Residential electrical Load Forecasting (SRLF) models. However, SRLF typically requires fine-grained user data as input, which raises privacy concerns. Moreover, smart meters, as computing resource-constrained devices, are unable to perform complex training processes locally. Furthermore, the large-scale deployment of smart meters imposes significant communication overhead and latency on networks. To address these challenges, we propose an end-edge-cloud hierarchical Federated Learning (FL) framework that leverages fine-grained residential load data from numerous smart meters in a privacy-preserving and resource-efficient manner. Specifically, the primary training and prediction tasks are shifted to edge servers proximate to the smart meters. An inner-product functional encryption scheme is employed, enabling local training and prediction to be performed by the edge server for multiple smart meters without their raw data being accessed. Furthermore, a hierarchical aggregation strategy ensures the equitable and comprehensive utilization of distributed data. Extensive experiments demonstrate our scheme achieving better performance in the practical environment.