mlsanityData to 7 Oct 2026

Energy Demand Prediction with Federated Learning for Electric Vehicle Networks

arXiv 1909.00907 ↗First posted 3 Sep 2019eess.SP cs.LGcited by 46

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Abstract (arXiv)

In this paper, we propose novel approaches using state-of-the-art machine learning techniques, aiming at predicting energy demand for electric vehicle (EV) networks. These methods can learn and find the correlation of complex hidden features to improve the prediction accuracy. First, we propose an energy demand learning (EDL)-based prediction solution in which a charging station provider (CSP) gathers information from all charging stations (CSs) and then performs the EDL algorithm to predict the energy demand for the considered area. However, this approach requires frequent data sharing between the CSs and the CSP, thereby driving communication overhead and privacy issues for the EVs and CSs. To address this problem, we propose a federated energy demand learning (FEDL) approach which allows the CSs sharing their information without revealing real datasets. Specifically, the CSs only need to send their trained models to the CSP for processing. In this case, we can significantly reduce the communication overhead and effectively protect data privacy for the EV users. To further improve the effectiveness of the FEDL, we then introduce a novel clustering-based EDL approach for EV networks by grouping the CSs into clusters before applying the EDL algorithms. Through experimental results, we show that our proposed approaches can improve the accuracy of energy demand prediction up to 24.63% and decrease communication overhead by 83.4% compared with other baseline machine learning algorithms.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Sep 2019.

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Citing papers per year: 2021: 12; 2022: 6; 2023: 11; 2024: 9; 2025: 6; 2026: 2.

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  3. 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
  4. 2021Real-time End-to-End Federated Learning: An Automotive Case Studycited by 15
  5. 2023Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problemscited by 15
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  8. 2021A Federated Learning Framework for Smart Grids: Securing Power Traces in Collaborative Learningcited by 11
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  10. 2021A Payload Optimization Method for Federated Recommender Systemscited by 10
  11. 2022Enhancing Heterogeneous Federated Learning with Knowledge Extraction and Multi-Model Fusioncited by 10
  12. 2024Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Datacited by 10
34 more
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  2. 2022A Systematic Literature Review on Blockchain Enabled Federated Learning Framework for Internet of Vehiclescited by 7
  3. 2021Wireless Federated Learning (WFL) for 6G Networks -- Part I: Research Challenges and Future Trendscited by 6
  4. 2021Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directionscited by 6
  5. 2023Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectivescited by 6
  6. 2025Federated Learning for Cyber Physical Systems: A Comprehensive Surveycited by 6
  7. 2021Sustainable Federated Learningcited by 5
  8. 2021Empowering Prosumer Communities in Smart Grid with Wireless Communications and Federated Edge Learningcited by 5
  9. 2024Federated Graph Learning for EV Charging Demand Forecasting with Personalization Against Cyberattackscited by 4
  10. 2024Save It All: Enabling Full Parameter Tuning for Federated Large Language Models via Cycle Block Gradient Descentcited by 4
  11. 2024A federated large language model for long-term time series forecastingcited by 4
  12. 2025FedGES: A Federated Learning Approach for BN Structure Learningcited by 4
  13. 2022Strategic Coalition for Data Pricing in IoT Data Marketscited by 3
  14. 2023CorrFL: Correlation-Based Neural Network Architecture for Unavailability Concerns in a Heterogeneous IoT Environmentcited by 3
  15. 2021Federated learning and next generation wireless communications: A survey on bidirectional relationshipcited by 2
  16. 2023Energy Prediction using Federated Learningcited by 2
  17. 2023Federated Learning Based Distributed Localization of False Data Injection Attacks on Smart Gridscited by 2
  18. 2024Smart Sampling: Helping from Friendly Neighbors for Decentralized Federated Learningcited by 2
  19. 2024Coherent Hierarchical Probabilistic Forecasting of Electric Vehicle Charging Demandcited by 2
  20. 2022A Fair and Efficient Hybrid Federated Learning Framework based on XGBoost for Distributed Power Predictioncited by 1
  21. 2023Applications of Federated Learning in Manufacturing: Identifying the Challenges and Exploring the Future Directions with Industry 4.0 and 5.0 Visionscited by 1
  22. 2023Privacy-Aware Data Acquisition under Data Similarity in Regression Marketscited by 1
  23. 2025Fairness in Federated Learning: Fairness for Whom?cited by 1
  24. 2026On Electric Vehicle Energy Demand Forecasting and the Effect of Federated Learningcited by 1
  25. 2022Momentum Gradient Descent Federated Learning with Local Differential Privacycited by 0
  26. 2023Communication-Efficient Design of Learning System for Energy Demand Forecasting of Electrical Vehiclescited by 0
  27. 2023Federated Battery Diagnosis and Prognosiscited by 0
  28. 2024Bridging Data Barriers among Participants: Assessing the Potential of Geoenergy through Federated Learningcited by 0
  29. 2024Towards Privacy-Aware and Personalised Assistive Robots: A User-Centred Approachcited by 0
  30. 2024Overpredictive Signal Analytics in Federated Learning: Algorithms and Analysiscited by 0
  31. 2025Learning with Limited Shared Information in Multi-agent Multi-armed Banditcited by 0
  32. 2025FedCCL: Federated Clustered Continual Learning Framework for Privacy-focused Energy Forecastingcited by 0
  33. 2025Federated Anomaly Detection and Mitigation for EV Charging Forecasting Under Cyberattackscited by 0
  34. 2026Federated Learning for Global Carbon Emission Forecasting: A Hybrid Time-Series Approach with Statistical and Neural Modelscited by 0