Summary of Communication-efficient Adaptive Batch Size Strategies For Distributed Local Gradient Methods, by Tim Tsz-kit Lau and Weijian Li and Chenwei Xu and Han Liu and Mladen Kolar
Communication-Efficient Adaptive Batch Size Strategies for Distributed Local Gradient Methods
by Tim Tsz-Kit Lau, Weijian Li, Chenwei Xu, Han Liu, Mladen Kolar
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); 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 A novel approach to distributed training in deep neural networks is presented, addressing communication overheads that arise when using multiple workers. Local gradient methods like Local SGD reduce communication by synchronizing model parameters and/or gradients after several local steps, but the optimal batch size for these methods remains unclear. The authors introduce adaptive batch size strategies that increase batch sizes adaptively to reduce minibatch gradient variance, providing guarantees of convergence under homogeneous data conditions. Experimental results in image classification and language modeling demonstrate the effectiveness of this approach in improving training efficiency and generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper explores ways to make deep learning models train faster and better using many computers working together. When you have a lot of workers, it’s hard for them to share information without slowing down. A solution is to use “local gradient methods” that only update the model after a few small steps. But figuring out how big these steps should be is tricky. The authors developed new ways to adjust the step size based on the data, and tested them with pictures and language tasks. They found that this approach makes training faster and more accurate. |
Keywords
» Artificial intelligence » Deep learning » Generalization » Image classification
