# Electrical Load Forecasting Using Edge Computing and Federated Learning

arXiv: https://arxiv.org/abs/2201.11248 · HTML: https://mlsanity.com/paper/2201.11248/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2022.

## Built on by (most cited first, 30 of 34)

- [Federated Learning for 5G Base Station Traffic Forecasting](https://mlsanity.com/paper/2211.15220/) (2022)
- [FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers](https://mlsanity.com/paper/2203.00219/) (2022)
- [Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey](https://mlsanity.com/paper/2505.01821/) (2025)
- [Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach](https://mlsanity.com/paper/2411.10619/) (2024)
- [Deep Learning for Time Series Forecasting: A Survey](https://mlsanity.com/paper/2503.10198/) (2025)
- [A Secure Federated Learning Framework for Residential Short Term Load Forecasting](https://mlsanity.com/paper/2209.14547/) (2022)
- [Federated Learning for Water Consumption Forecasting in Smart Cities](https://mlsanity.com/paper/2301.13036/) (2023)
- [Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning](https://mlsanity.com/paper/2502.17226/) (2025)
- [Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives](https://mlsanity.com/paper/2304.08602/) (2023)
- [Federated Learning for Cyber Physical Systems: A Comprehensive Survey](https://mlsanity.com/paper/2505.04873/) (2025)
- [Personalized Graph Federated Learning with Differential Privacy](https://mlsanity.com/paper/2306.06399/) (2023)
- [Federated Short-Term Load Forecasting with Personalization Layers for Heterogeneous Clients](https://mlsanity.com/paper/2309.13194/) (2023)
- [Energy Prediction using Federated Learning](https://mlsanity.com/paper/2301.09165/) (2023)
- [Survey of Federated Learning Models for Spatial-Temporal Mobility Applications](https://mlsanity.com/paper/2305.05257/) (2023)
- [Federated Learning Based Distributed Localization of False Data Injection Attacks on Smart Grids](https://mlsanity.com/paper/2306.10420/) (2023)
- [Secure short-term load forecasting for smart grids with transformer-based federated learning](https://mlsanity.com/paper/2310.17477/) (2023)
- [Exploring Lightweight Federated Learning for Distributed Load Forecasting](https://mlsanity.com/paper/2404.03320/) (2024)
- [pFedGame -- Decentralized Federated Learning using Game Theory in Dynamic Topology](https://mlsanity.com/paper/2410.04058/) (2024)
- [FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination](https://mlsanity.com/paper/2304.03640/) (2023)
- [Privacy-Preserving Load Forecasting via Personalized Model Obfuscation](https://mlsanity.com/paper/2312.00036/) (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)
- [On Vessel Location Forecasting and the Effect of Federated Learning](https://mlsanity.com/paper/2405.19870/) (2024)
- [On Electric Vehicle Energy Demand Forecasting and the Effect of Federated Learning](https://mlsanity.com/paper/2602.20782/) (2026)
- [Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets](https://mlsanity.com/paper/2206.11715/) (2022)
- [FedTrees: A Novel Computation-Communication Efficient Federated Learning Framework Investigated in Smart Grids](https://mlsanity.com/paper/2210.00060/) (2022)
- [SoK: Assessing the State of Applied Federated Machine Learning](https://mlsanity.com/paper/2308.02454/) (2023)
- [Federated Computing -- Survey on Building Blocks, Extensions and Systems](https://mlsanity.com/paper/2404.02779/) (2024)
- [Federated Learning Forecasting for Strengthening Grid Reliability and Enabling Markets for Resilience](https://mlsanity.com/paper/2407.11571/) (2024)
- [Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip Approach](https://mlsanity.com/paper/2407.15879/) (2024)

## Abstract (arXiv)

In the smart grid, huge amounts of consumption data are used to train deep learning models for applications such as load monitoring and demand response. However, these applications raise concerns regarding security and have high accuracy requirements. In one hand, the data used is privacy-sensitive. For instance, the fine-grained data collected by a smart meter at a consumer's home may reveal information on the appliances and thus the consumer's behaviour at home. On the other hand, the deep learning models require big data volumes with enough variety and to be trained adequately. In this paper, we evaluate the use of Edge computing and federated learning, a decentralized machine learning scheme that allows to increase the volume and diversity of data used to train the deep learning models without compromising privacy. This paper reports, to the best of our knowledge, the first use of federated learning for household load forecasting and achieves promising results. The simulations were done using Tensorflow Federated on the data from 200 houses from Texas, USA.