Summary of A Survey Of Distributed Learning in Cloud, Mobile, and Edge Settings, by Madison Threadgill et al.
A Survey of Distributed Learning in Cloud, Mobile, and Edge Settings
by Madison Threadgill, Andreas Gerstlauer
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Machine learning models are becoming increasingly complex, requiring significant computational resources for both inference and training stages. To address this challenge, researchers have turned to distributed learning, which employs parallelization across various devices and environments. This survey explores the landscape of distributed learning, examining data and model parallelism, partitioning schemes, and trade-offs between computational efficiency, communication overhead, and memory constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models are getting more complex and need a lot of computer power to work properly. To solve this problem, scientists have developed a way to share the workload across many devices. This study looks at how we divide up data and models to use all the available resources efficiently. We’ll explore different ways of doing this and how it affects the performance and memory usage. |
Keywords
» Artificial intelligence » Inference » Machine learning
