Summary of Grawa: Gradient-based Weighted Averaging For Distributed Training Of Deep Learning Models, by Tolga Dimlioglu et al.
GRAWA: Gradient-based Weighted Averaging for Distributed Training of Deep Learning Models
by Tolga Dimlioglu, Anna Choromanska
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Optimization and Control (math.OC)
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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 paper proposes a new algorithm for distributed training of deep learning models in time-constrained environments. The algorithm periodically pulls workers towards the center variable, prioritizing recovering flat regions in the optimization landscape. Two asynchronous variants are developed: Model-level and Layer-level Gradient-based Weighted Averaging (MGRAWA and LGRAWA), differing in their weighting schemes. Theoretical convergence guarantees are proven for both convex and non-convex settings. Experimental results show that the proposed algorithms outperform competitors, achieving faster convergence and better quality local optima. An ablation study analyzes scalability in crowded environments, revealing less frequent communication and fewer distributed updates compared to state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way for computers to train deep learning models together quickly. They want to make sure the training process is fast and good at finding the best solutions. To do this, they created an algorithm that helps the different parts of the model work together better. This makes it possible to find the best answers faster. The researchers tested their algorithm and found that it works better than other methods. It also requires less communication between the computers, which makes it more efficient. |
Keywords
* Artificial intelligence * Deep learning * Optimization
