Exploring Lightweight Federated Learning for Distributed Load Forecasting

arXiv:2404.03320v1 Announce Type: new
Abstract: Federated Learning (FL) is a distributed learning scheme that enables deep learning to be applied to sensitive data streams and applications in a privacy-preserving manner. This paper focuses on the use of FL for analyzing smart energy meter data with the aim to achieve comparable accuracy to state-of-the-art methods for load forecasting while ensuring the privacy of individual meter data. We show that with a lightweight fully connected deep neural network, we are able to …

abstract accuracy aim applications arxiv cs.lg cs.sy data data streams deep learning distributed distributed learning eess.sy energy federated learning forecasting paper privacy smart type