mlsanityData to 14 Oct 2026

Electrical Load Forecasting Using Edge Computing and Federated Learning

arXiv 2201.11248 ↗First posted 27 Jan 2022cs.CEcited by 34

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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.

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

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 5; 2023: 11; 2024: 9; 2025: 7; 2026: 2.

34 papers.

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  1. 2022Federated Learning for 5G Base Station Traffic Forecastingcited by 15
  2. 2022FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providerscited by 14
  3. 2025Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Surveycited by 14
  4. 2024Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approachcited by 11
  5. 2025Deep Learning for Time Series Forecasting: A Surveycited by 10
  6. 2022A Secure Federated Learning Framework for Residential Short Term Load Forecastingcited by 7
  7. 2023Federated Learning for Water Consumption Forecasting in Smart Citiescited by 7
  8. 2025Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learningcited by 7
  9. 2023Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectivescited by 6
  10. 2025Federated Learning for Cyber Physical Systems: A Comprehensive Surveycited by 6
  11. 2023Personalized Graph Federated Learning with Differential Privacycited by 4
  12. 2023Federated Short-Term Load Forecasting with Personalization Layers for Heterogeneous Clientscited by 3
22 more
  1. 2023Energy Prediction using Federated Learningcited by 2
  2. 2023Survey of Federated Learning Models for Spatial-Temporal Mobility Applicationscited by 2
  3. 2023Federated Learning Based Distributed Localization of False Data Injection Attacks on Smart Gridscited by 2
  4. 2023Secure short-term load forecasting for smart grids with transformer-based federated learningcited by 2
  5. 2024Exploring Lightweight Federated Learning for Distributed Load Forecastingcited by 2
  6. 2024pFedGame -- Decentralized Federated Learning using Game Theory in Dynamic Topologycited by 2
  7. 2023FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discriminationcited by 1
  8. 2023Privacy-Preserving Load Forecasting via Personalized Model Obfuscationcited by 1
  9. 2024Privacy-Preserving Collaborative Split Learning Framework for Smart Grid Load Forecastingcited by 1
  10. 2024Addressing Heterogeneity in Federated Load Forecasting with Personalization Layerscited by 1
  11. 2024On Vessel Location Forecasting and the Effect of Federated Learningcited by 1
  12. 2026On Electric Vehicle Energy Demand Forecasting and the Effect of Federated Learningcited by 1
  13. 2022Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Marketscited by 0
  14. 2022FedTrees: A Novel Computation-Communication Efficient Federated Learning Framework Investigated in Smart Gridscited by 0
  15. 2023SoK: Assessing the State of Applied Federated Machine Learningcited by 0
  16. 2024Federated Computing -- Survey on Building Blocks, Extensions and Systemscited by 0
  17. 2024Federated Learning Forecasting for Strengthening Grid Reliability and Enabling Markets for Resiliencecited by 0
  18. 2024Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip Approachcited by 0
  19. 2025WallStreetFeds: Client-Specific Tokens as Investment Vehicles in Federated Learningcited by 0
  20. 2025D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Datacited by 0
  21. 2025Optimizing Federated Learning for Scalable Power-demand Forecasting in Microgridscited by 0
  22. 2026Privacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmapcited by 0