Summary of Federated Learning For Smart Grid: a Survey on Applications and Potential Vulnerabilities, by Zikai Zhang et al.
Federated Learning for Smart Grid: A Survey on Applications and Potential Vulnerabilities
by Zikai Zhang, Suman Rath, Jiaohao Xu, Tingsong Xiao
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Smart Grid (SG) is a critical energy infrastructure that collects real-time electricity usage data using information and communication technologies (ICT). Due to growing concerns about data security and privacy, federated learning (FL) has emerged as a promising training framework. This paper surveys recent advancements in designing FL-based SG systems across three stages: generation, transmission and distribution, and consumption. The survey also explores potential vulnerabilities that may arise when implementing FL in these stages. Finally, the paper discusses the gap between state-of-the-art FL research and its practical applications in SGs and proposes future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Smart Grid is a way to manage electricity usage in real-time. To keep this data private and secure, researchers are using something called federated learning. This helps different devices share information without sharing personal details. The paper looks at how this works for the Smart Grid, from collecting data to using it. It also talks about potential problems that might happen when using this method. The main idea is to show how this technology can be used in the Smart Grid and what we need to do to make it better. |
Keywords
» Artificial intelligence » Federated learning
