Summary of High-dimensional Distributed Sparse Classification with Scalable Communication-efficient Global Updates, by Fred Lu et al.
High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates
by Fred Lu, Ryan R. Curtin, Edward Raff, Francis Ferraro, James Holt
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); 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 The proposed method addresses the limitations of distributed training for large datasets by developing a communication-efficient distributed logistic regression model. The approach optimizes a surrogate likelihood locally to iteratively improve on an initial solution, reducing the need for expensive communication between nodes. This leads to improved accuracy and faster runtimes compared to existing distributed algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has found a way to train models efficiently when working with very large datasets. They did this by developing a new method that can be used in parallel processing. This means that many computers can work together on the same task, without needing to share lots of information between them. The result is faster and more accurate training. |
Keywords
» Artificial intelligence » Likelihood » Logistic regression
