Federated Learning Forecasting for Strengthening Grid Reliability and Enabling Markets for Resilience
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Abstract (arXiv)
We propose a comprehensive approach to increase the reliability and resilience of future power grids rich in distributed energy resources. Our distributed scheme combines federated learning-based attack detection with a local electricity market-based attack mitigation method. We validate the scheme by applying it to a real-world distribution grid rich in solar PV. Simulation results demonstrate that the approach is feasible and can successfully mitigate the grid impacts of cyber-physical attacks.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 28 references that are in the map. First posted on arXiv 16 Jul 2024.
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References · 9
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
- 2021Towards Personalized Federated Learningcited by 464
- 2021Federated Learning on Non-IID Data: A Surveycited by 346
- 2022Electrical Load Forecasting Using Edge Computing and Federated Learningcited by 34
- 2022Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approachcited by 27
- 2021A Hierarchical Local Electricity Market for a DER-rich Grid Edgecited by 1
- 2023Local retail electricity markets for distribution grid servicescited by 1
- 2023Enhancing power grid resilience to cyber-physical attacks using distributed retail electricity marketscited by 1
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