# Privacy-preserving Federated Learning for Residential Short Term Load Forecasting

arXiv: https://arxiv.org/abs/2111.09248 · HTML: https://mlsanity.com/paper/2111.09248/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 115 references that are in the map.
- First posted on arXiv 17 Nov 2021.

## Builds on (most cited first, 19 of 19)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [Renyi Differential Privacy](https://mlsanity.com/paper/1702.07476/) (2017)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [Differentially Private Learning with Adaptive Clipping](https://mlsanity.com/paper/1905.03871/) (2019)
- [Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges](https://mlsanity.com/paper/1802.04117/) (2018)
- [Forecasting: theory and practice](https://mlsanity.com/paper/2012.03854/) (2020)
- [Building Energy Load Forecasting using Deep Neural Networks](https://mlsanity.com/paper/1610.09460/) (2016)
- [Optimizing Performance of Recurrent Neural Networks on GPUs](https://mlsanity.com/paper/1604.01946/) (2016)
- [Market Value of Differentially-Private Smart Meter Data](https://mlsanity.com/paper/2104.09898/) (2021)
- [AMI-FML: A Privacy-Preserving Federated Machine Learning Framework for AMI](https://mlsanity.com/paper/2109.05666/) (2021)

## Built on by (most cited first, 4 of 4)

- [Secure short-term load forecasting for smart grids with transformer-based federated learning](https://mlsanity.com/paper/2310.17477/) (2023)
- [Cross-Fusion Rule for Personalized Federated Learning](https://mlsanity.com/paper/2302.02531/) (2023)
- [Using ARIMA to Predict the Expansion of Subscriber Data Consumption](https://mlsanity.com/paper/2404.15095/) (2024)
- [TS-Inverse: A Gradient Inversion Attack Tailored for Federated Time Series Forecasting Models](https://mlsanity.com/paper/2503.20952/) (2025)

## Abstract (arXiv)

With high levels of intermittent power generation and dynamic demand patterns, accurate forecasts for residential loads have become essential. Smart meters can play an important role when making these forecasts as they provide detailed load data. However, using smart meter data for load forecasting is challenging due to data privacy requirements. This paper investigates how these requirements can be addressed through a combination of federated learning and privacy preserving techniques such as differential privacy and secure aggregation. For our analysis, we employ a large set of residential load data and simulate how different federated learning models and privacy preserving techniques affect performance and privacy. Our simulations reveal that combining federated learning and privacy preserving techniques can secure both high forecasting accuracy and near-complete privacy. Specifically, we find that such combinations enable a high level of information sharing while ensuring privacy of both the processed load data and forecasting models. Moreover, we identify and discuss challenges of applying federated learning, differential privacy and secure aggregation for residential short-term load forecasting.