Summary of Histogram-based Federated Xgboost Using Minimal Variance Sampling For Federated Tabular Data, by William Lindskog et al.
Histogram-Based Federated XGBoost using Minimal Variance Sampling for Federated Tabular Data
by William Lindskog, Christian Prehofer, Sarandeep Singh
First submitted to arxiv on: 3 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 federated learning approach for tabular data is proposed, which leverages Tree-Based Models (TBMs) such as XGBoost and Minimal Variance Sampling (MVS). The authors demonstrate a histogram-based federated XGBoost that uses MVS to improve performance in terms of accuracy and regression error in a federated setting. Compared to uniform and no sampling, the proposed model achieves better local and global performance on new federated tabular datasets. Moreover, it outperforms centralized XGBoost in half of the studied cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way for different devices or systems to work together without sharing their data. This can be useful when you have lots of data that’s too sensitive to share. The researchers looked at how well this works with a type of machine learning called Tree-Based Models (TBMs). They found that if they “sample” the data in a special way, it can make their model even better. They tested this on some fake data and saw that it worked really well. |
Keywords
» Artificial intelligence » Federated learning » Machine learning » Regression » Xgboost
