Summary of Exploring the Practicality Of Federated Learning: a Survey Towards the Communication Perspective, by Khiem Le et al.
Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspective
by Khiem Le, Nhan Luong-Ha, Manh Nguyen-Duc, Danh Le-Phuoc, Cuong Do, Kok-Seng Wong
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 survey explores various strategies to improve the communication efficiency of Federated Learning (FL) systems, which enables collaborative model training without centralizing data. The authors analyze the sources of inefficiency and provide a taxonomy and comprehensive review of state-of-the-art methods to overcome the communication challenges. They also discuss future research directions for enhancing FL’s communication efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way to train models without sharing private data. It works by letting many devices work together to update a model, but this process can be slow and inefficient due to the amount of information that needs to be exchanged. Researchers have been working on ways to make this process faster and more efficient. This survey looks at some of the most effective strategies they’ve come up with. |
Keywords
» Artificial intelligence » Federated learning
