# Federated Learning Forecasting for Strengthening Grid Reliability and Enabling Markets for Resilience

arXiv: https://arxiv.org/abs/2407.11571 · HTML: https://mlsanity.com/paper/2407.11571/

## 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.

## Builds on (most cited first, 9 of 9)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [Electrical Load Forecasting Using Edge Computing and Federated Learning](https://mlsanity.com/paper/2201.11248/) (2022)
- [Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach](https://mlsanity.com/paper/2209.00778/) (2022)
- [A Hierarchical Local Electricity Market for a DER-rich Grid Edge](https://mlsanity.com/paper/2110.02358/) (2021)
- [Local retail electricity markets for distribution grid services](https://mlsanity.com/paper/2302.06591/) (2023)
- [Enhancing power grid resilience to cyber-physical attacks using distributed retail electricity markets](https://mlsanity.com/paper/2311.05145/) (2023)

## 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.