A review of federated learning in renewable energy applications: Potential, challenges, and future directions
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Abstract (arXiv)
Federated learning has recently emerged as a privacy-preserving distributed machine learning approach. Federated learning enables collaborative training of multiple clients and entire fleets without sharing the involved training datasets. By preserving data privacy, federated learning has the potential to overcome the lack of data sharing in the renewable energy sector which is inhibiting innovation, research and development. Our paper provides an overview of federated learning in renewable energy applications. We discuss federated learning algorithms and survey their applications and case studies in renewable energy generation and consumption. We also evaluate the potential and the challenges associated with federated learning applied in power and energy contexts. Finally, we outline promising future research directions in federated learning for applications in renewable energy.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 50 of its 122 references that are in the map. First posted on arXiv 18 Dec 2023.
Builds on
50 of 122 references.
References · 50
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2019Advances and Open Problems in Federated Learningcited by 3,021
- 2018Federated Optimization in Heterogeneous Networkscited by 2,971
- 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
- 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
- 2014How transferable are features in deep neural networks?cited by 1,216
- 2019A Comprehensive Survey on Transfer Learningcited by 1,154
- 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
- 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
- 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
- 2020Federated Learning with Matched Averagingcited by 564
- 2019Federated Learning with Personalization Layerscited by 533
38 more
- 2021Towards Personalized Federated Learningcited by 462
- 2019Agnostic Federated Learningcited by 451
- 2019Federated Machine Learning: Concept and Applicationscited by 409
- 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
- 2020Adaptive Personalized Federated Learningcited by 353
- 2021Federated Learning on Non-IID Data: A Surveycited by 345
- 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 322
- 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
- 2019Differential Privacy Has Disparate Impact on Model Accuracycited by 316
- 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 222
- 2017Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?cited by 220
- 2020Privacy and Robustness in Federated Learning: Attacks and Defensescited by 163
- 2017A Survey on Homomorphic Encryption Schemes: Theory and Implementationcited by 154
- 2019Federated Evaluation of On-device Personalizationcited by 147
- 2020Survey of Personalization Techniques for Federated Learningcited by 144
- 2020Salvaging Federated Learning by Local Adaptationcited by 142
- 2022Federated Learning with Partial Model Personalizationcited by 114
- 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 112
- 2020Rethinking the Hyperparameters for Fine-tuningcited by 69
- 2021Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharingcited by 58
- 2022FedAvg with Fine Tuning: Local Updates Lead to Representation Learningcited by 55
- 2021Communication Efficiency in Federated Learning: Achievements and Challengescited by 40
- 2021A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparisoncited by 31
- 2022Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approachcited by 27
- 2022Towards Efficient Communications in Federated Learning: A Contemporary Surveycited by 24
- 2021Privacy-preserving Spatiotemporal Scenario Generation of Renewable Energies: A Federated Deep Generative Learning Approachcited by 22
- 2019Federated Uncertainty-Aware Learning for Distributed Hospital EHR Datacited by 17
- 2020A Federated Learning Approach to Anomaly Detection in Smart Buildingscited by 17
- 2020A Systematic Literature Review on Federated Learning: From A Model Quality Perspectivecited by 15
- 2021Federated Learning for Short-term Residential Load Forecastingcited by 13
- 2022Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approachcited by 12
- 2022Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Managementcited by 11
- 2022Asynchronous Decentralized Federated Learning for Collaborative Fault Diagnosis of PV Stationscited by 9
- 2022A Review of Federated Learning in Energy Systemscited by 9
- 2021Federated Asymptotics: a model to compare federated learning algorithmscited by 7
- 2021Privacy Assessment of Federated Learning using Private Personalized Layerscited by 5
- 2023Privacy-Aware Energy Consumption Modeling of Connected Battery Electric Vehicles using Federated Learningcited by 4
- 2022Towards Fleet-wide Sharing of Wind Turbine Condition Information through Privacy-preserving Federated Learningcited by 2
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Citing papers per year: 2024: 1; 2025: 1; 2026: 1.
3 papers.