Summary of Smart Information Exchange For Unsupervised Federated Learning Via Reinforcement Learning, by Seohyun Lee et al.
Smart Information Exchange for Unsupervised Federated Learning via Reinforcement Learning
by Seohyun Lee, Anindya Bijoy Das, Satyavrat Wagle, Christopher G. Brinton
First submitted to arxiv on: 15 Feb 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 This paper proposes a novel approach to decentralized machine learning, specifically Federated Learning (FL), by creating an optimal graph for data transfer using Reinforcement Learning. The goal is to form links that provide the most benefit considering environmental constraints, improving convergence speed in unsupervised FL environments. Numerical analysis shows advantages in terms of convergence speed and straggler resilience compared to different available FL schemes and benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem with decentralized machine learning called Federated Learning. When devices share data with each other, it can be tricky because the data is not labeled. The researchers found a way to make devices exchange data in an unsupervised way using Reinforcement Learning. This makes the process faster and more reliable. They tested their idea on different datasets and showed that it works better than other methods. |
Keywords
* Artificial intelligence * Federated learning * Machine learning * Reinforcement learning * Unsupervised
