# Secure short-term load forecasting for smart grids with transformer-based federated learning

arXiv: https://arxiv.org/abs/2310.17477 · HTML: https://mlsanity.com/paper/2310.17477/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 38 references that are in the map.
- First posted on arXiv 26 Oct 2023.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [Electrical Load Forecasting Using Edge Computing and Federated Learning](https://mlsanity.com/paper/2201.11248/) (2022)
- [Federated Learning for Short-term Residential Load Forecasting](https://mlsanity.com/paper/2105.13325/) (2021)
- [DER Forecast using Privacy Preserving Federated Learning](https://mlsanity.com/paper/2107.03248/) (2021)
- [Federated Learning based Energy Demand Prediction with Clustered Aggregation](https://mlsanity.com/paper/2210.15850/) (2022)
- [A Federated DRL Approach for Smart Micro-Grid Energy Control with Distributed Energy Resources](https://mlsanity.com/paper/2211.03430/) (2022)
- [Privacy-preserving Federated Learning for Residential Short Term Load Forecasting](https://mlsanity.com/paper/2111.09248/) (2021)

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

- [D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data](https://mlsanity.com/paper/2507.11471/) (2025)
- [Optimizing Federated Learning for Scalable Power-demand Forecasting in Microgrids](https://mlsanity.com/paper/2508.08022/) (2025)

## Abstract (arXiv)

Electricity load forecasting is an essential task within smart grids to assist demand and supply balance. While advanced deep learning models require large amounts of high-resolution data for accurate short-term load predictions, fine-grained load profiles can expose users' electricity consumption behaviors, which raises privacy and security concerns. One solution to improve data privacy is federated learning, where models are trained locally on private data, and only the trained model parameters are merged and updated on a global server. Therefore, this paper presents a novel transformer-based deep learning approach with federated learning for short-term electricity load prediction. To evaluate our results, we benchmark our federated learning architecture against central and local learning and compare the performance of our model to long short-term memory models and convolutional neural networks. Our simulations are based on a dataset from a German university campus and show that transformer-based forecasting is a promising alternative to state-of-the-art models within federated learning.