mlsanityData to 14 Oct 2026

Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives

arXiv 2304.08602 ↗First posted 17 Apr 2023cs.LG cs.AI cs.CRcited by 6

Not on the map. Why?

Abstract (arXiv)

Consumer's privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy data, particularly when used to train machine learning models for different services. These data-driven models often require huge amounts of data to achieve acceptable performance leading in most cases to risks of privacy leakage. By pushing the training to the edge, Federated Learning (FL) offers a good compromise between privacy preservation and the predictive performance of these models. The current paper presents an overview of FL applications in SGs while discussing their advantages and drawbacks, mainly in load forecasting, electric vehicles, fault diagnoses, load disaggregation and renewable energies. In addition, an analysis of main design trends and possible taxonomies is provided considering data partitioning, the communication topology, and security mechanisms. Towards the end, an overview of main challenges facing this technology and potential future directions is presented.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 31 of its 159 references that are in the map. First posted on arXiv 17 Apr 2023.

Builds on

31 of 159 references.

References · 31

  1. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  2. 2019Federated Machine Learning: Concept and Applicationscited by 412
  3. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  4. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 252
  5. 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 222
  6. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 139
  7. 2017Cross-lingual Entity Alignment via Joint Attribute-Preserving Embeddingcited by 127
  8. 2021Federated Learning: Opportunities and Challengescited by 84
  9. 2015Neural NILM: Deep Neural Networks Applied to Energy Disaggregationcited by 54
  10. 2019Energy Demand Prediction with Federated Learning for Electric Vehicle Networkscited by 46
  11. 2022Electrical Load Forecasting Using Edge Computing and Federated Learningcited by 34
  12. 2020Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directionscited by 25
19 more
  1. 2021Privacy-preserving Spatiotemporal Scenario Generation of Renewable Energies: A Federated Deep Generative Learning Approachcited by 22
  2. 2020A Federated Learning Approach to Anomaly Detection in Smart Buildingscited by 17
  3. 2022FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providerscited by 14
  4. 2021Federated Learning for Short-term Residential Load Forecastingcited by 13
  5. 2021Federated Learning with Hyperparameter-based Clustering for Electrical Load Forecastingcited by 13
  6. 2021A Federated Learning Framework for Smart Grids: Securing Power Traces in Collaborative Learningcited by 11
  7. 2021FedNILM: Applying Federated Learning to NILM Applications at the Edgecited by 11
  8. 2021DER Forecast using Privacy Preserving Federated Learningcited by 10
  9. 2020The emergence of Explainability of Intelligent Systems: Delivering Explainable and Personalised Recommendations for Energy Efficiencycited by 9
  10. 2022Asynchronous Decentralized Federated Learning for Collaborative Fault Diagnosis of PV Stationscited by 9
  11. 2022A Review of Federated Learning in Energy Systemscited by 9
  12. 2022Federated Learning based Energy Demand Prediction with Clustered Aggregationcited by 9
  13. 2021Intelligent edge-based recommender system for internet of energy applicationscited by 7
  14. 2021Empowering Prosumer Communities in Smart Grid with Wireless Communications and Federated Edge Learningcited by 5
  15. 2021Fed-BEV: A Federated Learning Framework for Modelling Energy Consumption of Battery Electric Vehiclescited by 5
  16. 2020Augmenting an Assisted Living Lab with Non-Intrusive Load Monitoringcited by 3
  17. 2013Smart Microgrids: Overview and Outlookcited by 2
  18. 2020Connecting Distributed Pockets of EnergyFlexibility through Federated Computations:Limitations and Possibilitiescited by 2
  19. 2021A Federated Learning Framework for Non-Intrusive Load Monitoringcited by 2

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 2; 2025: 2.

6 papers.

  1. 2023Federated Learning for Computer Visioncited by 22
  2. 2025SyncFed: Time-Aware Federated Learning through Explicit Timestamping and Synchronizationcited by 4
  3. 2024Federated and Transfer Learning for Cancer Detection Based on Image Analysiscited by 3
  4. 2023Edge AI for Internet of Energy: Challenges and Perspectivescited by 1
  5. 2024Unveiling Hidden Energy Anomalies: Harnessing Deep Learning to Optimize Energy Management in Sports Facilitiescited by 0
  6. 2025TS-Inverse: A Gradient Inversion Attack Tailored for Federated Time Series Forecasting Modelscited by 0