Submission · anonymous

Federated Learning for Short-Term Residential Load Forecasting

This paper investigates federated learning for short-term residential load forecasting, addressing privacy concerns in smart grid data. Local LSTM models are trained on individual household data and aggregated using FedAvg, demonstrating competitive accuracy compared to centralised training while preserving data privacy.