Summary of Framework For Co-distillation Driven Federated Learning to Address Class Imbalance in Healthcare, by Suraj Racha et al.
Framework for Co-distillation Driven Federated Learning to Address Class Imbalance in Healthcare
by Suraj Racha, Shubh Gupta, Humaira Firdowse, Aastik Solanki, Ganesh Ramakrishnan, Kshitij S. Jadhav
First submitted to arxiv on: 15 Nov 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 The proposed framework for Federated Learning (FL) tackles the issue of class imbalance and bias towards majority classes in a federated healthcare setting. By promoting knowledge sharing among clients through co-distillation, the framework improves learning outcomes. Experimental results show that it outperforms other federated methods in handling class imbalance, with the least standard deviation and robustness for increasing imbalance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning helps doctors share medical images without sharing personal information. But when some hospitals have better resources than others, it can be unfair to smaller hospitals. This paper solves this problem by letting all hospitals help each other learn together. It shows that this way is better at handling different amounts of data between hospitals and is more robust. |
Keywords
» Artificial intelligence » Distillation » Federated learning
