Summary of Truncated Non-uniform Quantization For Distributed Sgd, by Guangfeng Yan et al.
Truncated Non-Uniform Quantization for Distributed SGD
by Guangfeng Yan, Tan Li, Yuanzhang Xiao, Congduan Li, Linqi Song
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 two-stage quantization strategy to enhance the communication efficiency of distributed Stochastic Gradient Descent (SGD) in addressing the communication bottleneck challenge. The method first employs truncation to mitigate long-tail noise, followed by non-uniform quantization based on statistical characteristics. Theoretical guarantees are established for performance convergence, and optimal closed-form solutions are derived for truncation threshold and quantization levels under given constraints. Experimental evaluations show that the proposed algorithm outperforms existing schemes in terms of communication efficiency and convergence performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a problem with sharing information between computers doing machine learning tasks together. They make a new way to reduce how much information needs to be shared, called quantization. It works by first cutting off some of the extra noise that can happen when computers share information, then making the remaining information more compact and efficient. The researchers show that their method is better than other similar methods at balancing reducing unnecessary information with keeping the learning process working well. |
Keywords
* Artificial intelligence * Machine learning * Quantization * Stochastic gradient descent
