Summary of Communication-efficient Federated Learning with Adaptive Compression Under Dynamic Bandwidth, by Ying Zhuansun et al.
Communication-Efficient Federated Learning with Adaptive Compression under Dynamic Bandwidth
by Ying Zhuansun, Dandan Li, Xiaohong Huang, Caijun Sun
First submitted to arxiv on: 6 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper proposes a new federated learning algorithm called AdapComFL, which addresses the issue of large communication overhead in training models without sharing local data. By leveraging adaptive compression and bandwidth prediction, each client can optimize its model updates based on its available network resources. The authors also compare their approach with existing methods using real-world bandwidth data and benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers created a new way to train AI models without sending all the data to one place. They want to make sure that everyone’s internet connection is used efficiently. To do this, they developed an algorithm called AdapComFL. It looks at how fast each person’s internet is and adjusts how much information it sends accordingly. This makes training faster and more efficient. |
Keywords
» Artificial intelligence » Federated learning
