Summary of Robust Training Of Federated Models with Extremely Label Deficiency, by Yonggang Zhang et al.
Robust Training of Federated Models with Extremely Label Deficiency
by Yonggang Zhang, Zhiqin Yang, Xinmei Tian, Nannan Wang, Tongliang Liu, Bo Han
First submitted to arxiv on: 22 Feb 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 In this paper, researchers tackle the challenge of training machine learning models when data is distributed and some of it lacks labels. They propose a new approach called Twin-sight, which uses two models to learn from both labeled and unlabeled data simultaneously. This approach helps to reduce conflicts between different types of data by introducing constraints that preserve the relationships between features extracted from both models. The authors demonstrate the effectiveness of Twin-sight through experiments on four benchmark datasets, showing significant improvements over existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to learn using machine learning models when we don’t have enough labeled data. It’s like trying to teach someone without giving them clear instructions. The researchers came up with an idea called Twin-sight that uses two different models to work together and learn from both the labeled and unlabeled data. They want to see if this helps make better predictions by reducing any conflicts between the different types of data. |
Keywords
* Artificial intelligence * Machine learning
