# A Secure Federated Learning Framework for Residential Short Term Load Forecasting

arXiv: https://arxiv.org/abs/2209.14547 · HTML: https://mlsanity.com/paper/2209.14547/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 55 references that are in the map.
- First posted on arXiv 29 Sep 2022.

## Builds on (most cited first, 28 of 28)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://mlsanity.com/paper/1803.01498/) (2018)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [iDLG: Improved Deep Leakage from Gradients](https://mlsanity.com/paper/2001.02610/) (2020)
- [FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping](https://mlsanity.com/paper/2012.13995/) (2020)
- [The Hidden Vulnerability of Distributed Learning in Byzantium](https://mlsanity.com/paper/1802.07927/) (2018)
- [Robust Aggregation for Federated Learning](https://mlsanity.com/paper/1912.13445/) (2019)
- [RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets](https://mlsanity.com/paper/1811.03761/) (2018)
- [Protection Against Reconstruction and Its Applications in Private Federated Learning](https://mlsanity.com/paper/1812.00984/) (2018)
- [Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges](https://mlsanity.com/paper/2201.08135/) (2022)
- [Federated Variance-Reduced Stochastic Gradient Descent with Robustness to Byzantine Attacks](https://mlsanity.com/paper/1912.12716/) (2019)
- [From Local SGD to Local Fixed-Point Methods for Federated Learning](https://mlsanity.com/paper/2004.01442/) (2020)
- [Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging](https://mlsanity.com/paper/1909.05125/) (2019)
- [Attack-Resistant Federated Learning with Residual-based Reweighting](https://mlsanity.com/paper/1912.11464/) (2019)
- [Towards General Deep Leakage in Federated Learning](https://mlsanity.com/paper/2110.09074/) (2021)
- [Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees](https://mlsanity.com/paper/2002.10940/) (2020)
- [Electrical Load Forecasting Using Edge Computing and Federated Learning](https://mlsanity.com/paper/2201.11248/) (2022)
- [Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms](https://mlsanity.com/paper/1906.01736/) (2019)
- [One Parameter Defense -- Defending against Data Inference Attacks via Differential Privacy](https://mlsanity.com/paper/2203.06580/) (2022)
- [FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers](https://mlsanity.com/paper/2203.00219/) (2022)
- [Byzantine-Resilient Stochastic Gradient Descent for Distributed Learning: A Lipschitz-Inspired Coordinate-wise Median Approach](https://mlsanity.com/paper/1909.04532/) (2019)
- [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)
- [Towards Federated Learning With Byzantine-Robust Client Weighting](https://mlsanity.com/paper/2004.04986/) (2020)
- [Security Aspects of Internet of Things aided Smart Grids: a Bibliometric Survey](https://mlsanity.com/paper/2005.00915/) (2020)
- [False Data Injection Threats in Active Distribution Systems: A Comprehensive Survey](https://mlsanity.com/paper/2111.14251/) (2021)
- [A Taxonomy of Cyber Defence Strategies Against False Data Attacks in Smart Grid](https://mlsanity.com/paper/2103.16085/) (2021)

## Built on by (most cited first, 7 of 7)

- [Task-Aware Machine Unlearning and Its Application in Load Forecasting](https://mlsanity.com/paper/2308.14412/) (2023)
- [Federated Short-Term Load Forecasting with Personalization Layers for Heterogeneous Clients](https://mlsanity.com/paper/2309.13194/) (2023)
- [FeDiSa: A Semi-asynchronous Federated Learning Framework for Power System Fault and Cyberattack Discrimination](https://mlsanity.com/paper/2303.16956/) (2023)
- [FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination](https://mlsanity.com/paper/2304.03640/) (2023)
- [Privacy-Preserving Collaborative Split Learning Framework for Smart Grid Load Forecasting](https://mlsanity.com/paper/2403.01438/) (2024)
- [Addressing Heterogeneity in Federated Load Forecasting with Personalization Layers](https://mlsanity.com/paper/2404.01517/) (2024)
- [Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip Approach](https://mlsanity.com/paper/2407.15879/) (2024)

## Abstract (arXiv)

Smart meter measurements, though critical for accurate demand forecasting, face several drawbacks including consumers' privacy, data breach issues, to name a few. Recent literature has explored Federated Learning (FL) as a promising privacy-preserving machine learning alternative which enables collaborative learning of a model without exposing private raw data for short term load forecasting. Despite its virtue, standard FL is still vulnerable to an intractable cyber threat known as Byzantine attack carried out by faulty and/or malicious clients. Therefore, to improve the robustness of federated short-term load forecasting against Byzantine threats, we develop a state-of-the-art differentially private secured FL-based framework that ensures the privacy of the individual smart meter's data while protect the security of FL models and architecture. Our proposed framework leverages the idea of gradient quantization through the Sign Stochastic Gradient Descent (SignSGD) algorithm, where the clients only transmit the `sign' of the gradient to the control centre after local model training. As we highlight through our experiments involving benchmark neural networks with a set of Byzantine attack models, our proposed approach mitigates such threats quite effectively and thus outperforms conventional Fed-SGD models.