Summary of The Limits and Potentials Of Local Sgd For Distributed Heterogeneous Learning with Intermittent Communication, by Kumar Kshitij Patel et al.
The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication
by Kumar Kshitij Patel, Margalit Glasgow, Ali Zindari, Lingxiao Wang, Sebastian U. Stich, Ziheng Cheng, Nirmit Joshi, Nathan Srebro
First submitted to arxiv on: 19 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Optimization and Control (math.OC); Machine Learning (stat.ML)
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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 investigates the optimization method Local SGD, which has been shown to outperform other algorithms in practice. Despite its success, the theoretical underpinnings of Local SGD’s effectiveness have been lacking, leading to a gap between theory and practice. The authors provide new lower bounds for Local SGD under existing data heterogeneity assumptions, showing that these assumptions are insufficient to prove the effectiveness of local update steps. Additionally, they demonstrate the min-max optimality of accelerated mini-batch SGD for several problem classes. The results highlight the need for better models of data heterogeneity to understand Local SGD’s performance in practice. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Local SGD is a popular way to optimize machine learning models when working with distributed data. This method has been shown to work well in many situations, but it’s not fully understood why. In this paper, researchers try to fill this knowledge gap by studying the conditions under which Local SGD works best. They find that existing assumptions about how data is spread out are too weak to explain why Local SGD performs well. Instead, they show that a different approach, accelerated mini-batch SGD, is actually better in many cases. |
Keywords
» Artificial intelligence » Machine learning » Optimization
