mlsanityData to 7 Oct 2026

A Review of Federated Learning in Energy Systems

arXiv 2208.10941 ↗First posted 20 Aug 2022cs.CR cs.LGcited by 9

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Abstract (arXiv)

With increasing concerns for data privacy and ownership, recent years have witnessed a paradigm shift in machine learning (ML). An emerging paradigm, federated learning (FL), has gained great attention and has become a novel design for machine learning implementations. FL enables the ML model training at data silos under the coordination of a central server, eliminating communication overhead and without sharing raw data. In this paper, we conduct a review of the FL paradigm and, in particular, compare the types, the network structures, and the global model aggregation methods. Then, we conducted a comprehensive review of FL applications in the energy domain (refer to the smart grid in this paper). We provide a thematic classification of FL to address a variety of energy-related problems, including demand response, identification, prediction, and federated optimizations. We describe the taxonomy in detail and conclude with a discussion of various aspects, including challenges, opportunities, and limitations in its energy informatics applications, such as energy system modeling and design, privacy, and evolution.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 14 of its 42 references that are in the map. First posted on arXiv 20 Aug 2022.

Builds on

14 of 42 references.

References · 14

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  5. 2020Federated Learning with Matched Averagingcited by 564
  6. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
  7. 2020Multi-Participant Multi-Class Vertical Federated Learningcited by 47
  8. 2019Energy Demand Prediction with Federated Learning for Electric Vehicle Networkscited by 46
  9. 2021Federated Learning Meets Natural Language Processing: A Surveycited by 42
  10. 2022Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Surveycited by 31
  11. 2021Privacy-preserving Spatiotemporal Scenario Generation of Renewable Energies: A Federated Deep Generative Learning Approachcited by 22
  12. 2020A Federated Learning Approach to Anomaly Detection in Smart Buildingscited by 17
2 more
  1. 2021Empowering Prosumer Communities in Smart Grid with Wireless Communications and Federated Edge Learningcited by 5
  2. 2021Fed-NILM: A Federated Learning-based Non-Intrusive Load Monitoring Method for Privacy-Protectioncited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 2; 2025: 1; 2026: 3.

9 papers.

  1. 2023Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectivescited by 6
  2. 2023A review of federated learning in renewable energy applications: Potential, challenges, and future directionscited by 3
  3. 2024Enhancing Privacy in Federated Learning through Local Trainingcited by 3
  4. 2024Federated Learning for Smart Grid: A Survey on Applications and Potential Vulnerabilitiescited by 3
  5. 2025Federated Learning from Molecules to Processes: A Perspectivecited by 2
  6. 2026Forecasting Energy Availability in Local Energy Communities via LSTM Federated Learningcited by 1
  7. 2023Deep Convolutional Neural Networks for Short-Term Multi-Energy Demand Prediction of Integrated Energy Systemscited by 0
  8. 2026A Behaviour-Aware Federated Forecasting Framework for Distributed Stand-Alone Wind Turbinescited by 0
  9. 2026Constraint-Aware Aggregation for Federated Reinforcement Learning in Microgrid Energy Coordinationcited by 0