# Exploring Lightweight Federated Learning for Distributed Load Forecasting

arXiv: https://arxiv.org/abs/2404.03320 · HTML: https://mlsanity.com/paper/2404.03320/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 4 Apr 2024.

## Builds on (most cited first, 5 of 5)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Electrical Load Forecasting Using Edge Computing and Federated Learning](https://mlsanity.com/paper/2201.11248/) (2022)
- [Federated and Differentially Private Learning for Electronic Health Records](https://mlsanity.com/paper/1911.05861/) (2019)
- [Federated Learning with Hyperparameter-based Clustering for Electrical Load Forecasting](https://mlsanity.com/paper/2111.07462/) (2021)
- [Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning](https://mlsanity.com/paper/2309.05505/) (2023)

## Built on by (most cited first, 2 of 2)

- [TS-Inverse: A Gradient Inversion Attack Tailored for Federated Time Series Forecasting Models](https://mlsanity.com/paper/2503.20952/) (2025)
- [Optimizing Federated Learning for Scalable Power-demand Forecasting in Microgrids](https://mlsanity.com/paper/2508.08022/) (2025)

## Abstract (arXiv)

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 achieve forecasting accuracy comparable to existing schemes, both at each meter source and at the aggregator, by utilising the FL framework. The use of lightweight models further reduces the energy and resource consumption caused by complex deep-learning models, making this approach ideally suited for deployment across resource-constrained smart meter systems. With our proposed lightweight model, we are able to achieve an overall average load forecasting RMSE of 0.17, with the model having a negligible energy overhead of 50 mWh when performing training and inference on an Arduino Uno platform.