Summary of Sparse-proxskip: Accelerated Sparse-to-sparse Training in Federated Learning, by Georg Meinhardt et al.
Sparse-ProxSkip: Accelerated Sparse-to-Sparse Training in Federated Learning
by Georg Meinhardt, Kai Yi, Laurent Condat, Peter Richtárik
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 Federated Learning (FL) tackles the challenges of client resource constraints and communication costs by integrating sparse training and acceleration techniques. Recent research has shown that local training can improve communication complexity, but combining this with sparse training leads to suboptimal results. This paper introduces Sparse-ProxSkip, a method that addresses this issue by incorporating Straight-Through Estimator pruning into sparse training. Experimental results demonstrate the effectiveness of Sparse-ProxSkip in FL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In Federated Learning (FL), there are two main problems: client resource constraints and communication costs. To solve these issues, researchers have tried different techniques like sparse training and local training. But a new approach shows that combining these methods doesn’t work well. This paper solves this problem by creating a new method called Sparse-ProxSkip. It’s an efficient way to train models in FL while saving resources. |
Keywords
» Artificial intelligence » Federated learning » Pruning
