Summary of Gradient Coding in Decentralized Learning For Evading Stragglers, by Chengxi Li and Mikael Skoglund
Gradient Coding in Decentralized Learning for Evading Stragglers
by Chengxi Li, Mikael Skoglund
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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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 decentralized learning method called GOCO (Gossip-based Decentralized Learning Method with Gradient Coding) that addresses the issue of stragglers in distributed learning scenarios. Traditional gradient coding techniques are not directly applicable to decentralized learning, so the authors develop a new approach that combines gossip-based averaging with stochastic gradient coding. The proposed method updates parameter vectors locally using encoded gradients and then averages them in a gossip-based manner. The paper analyzes the convergence performance of GOCO for strongly convex loss functions and provides simulation results demonstrating its superiority over baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to learn together with other devices without a central computer, when some devices might not finish their job on time. They call this “stragglers”. Right now, we don’t have a good solution for this problem in decentralized learning. The authors suggest a new method called GOCO that combines two ideas: gossip-based averaging and gradient coding. This helps devices learn together more efficiently. They show that GOCO works well and is better than other methods. |
