Summary of Federated Automated Feature Engineering, by Tom Overman et al.
Federated Automated Feature Engineering
by Tom Overman, Diego Klabjan
First submitted to arxiv on: 5 Dec 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 The proposed automated feature engineering (AutoFE) methods aim to improve predictive performance in federated learning (FL) settings without requiring significant human intervention or expertise. By introducing novel algorithms for horizontal, vertical, and hybrid FL scenarios, the authors contribute to a relatively unexplored area of AutoFE research. Specifically, they develop approaches that can be applied to FL settings where data is aggregated across multiple clients without being shared between them. The results suggest that federated AutoFE achieves similar downstream model performance compared to traditional centralized data processing and feature engineering. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this groundbreaking study, researchers developed new algorithms for automating feature engineering in federated learning. This means they created ways to improve the accuracy of predictions without needing people to manually create new features or share their data with others. The authors focused on three types of FL settings: horizontal, vertical, and hybrid. Their approach can be used when data is collected from many sources but not shared between them. The results show that this federated AutoFE performs similarly well as traditional methods where all the data is held in one place. |
Keywords
» Artificial intelligence » Feature engineering » Federated learning
