Summary of Training Machine Learning Models at the Edge: a Survey, by Aymen Rayane Khouas et al.
Training Machine Learning models at the Edge: A Survey
by Aymen Rayane Khouas, Mohamed Reda Bouadjenek, Hakim Hacid, Sunil Aryal
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 In this survey, researchers explore the concept of edge learning, focusing on optimizing Machine Learning (ML) model training at the edge. By analyzing diverse approaches and methodologies in edge learning, identifying challenges, and highlighting future trends, the authors provide a comprehensive overview of the current landscape and future directions at the intersection of edge computing and machine learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Edge computing allows for AI capabilities to be integrated closer to where data is generated, improving efficiency. While most focus has been on deploying and inferring ML models at the edge, training these models remains less explored. This survey looks into optimizing ML model training at the edge, discussing distributed learning methods like federated learning. |
Keywords
* Artificial intelligence * Federated learning * Machine learning
