Summary of Asymmetrical Reciprocity-based Federated Learning For Resolving Disparities in Medical Diagnosis, by Jiaqi Wang et al.
Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical Diagnosis
by Jiaqi Wang, Ziyi Yin, Quanzeng You, Lingjuan Lyu, Fenglong Ma
First submitted to arxiv on: 27 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 This federated learning framework, called FedHelp, aims to address geographic health disparities by enhancing healthcare quality in underserved regions of low- and middle-income nations. Federated learning leverages support from medically more developed areas, but traditional approaches face challenges due to data scarcity and limited computation resources. To overcome these issues, FedHelp utilizes foundational model knowledge via one-time API access to guide the learning process of small clients with insufficient data. Additionally, an asymmetric dual knowledge distillation module manages the issue of asymmetric reciprocity between developed large clients and underserved small clients. The framework is validated through experiments on medical image classification and segmentation tasks, demonstrating significant performance improvements over state-of-the-art baselines, particularly benefiting clients in underserved regions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FedHelp is a new way to help people in poor areas get better healthcare. Right now, it’s hard for them to use powerful computer models because they don’t have enough medical data or computers that can handle complex tasks. To fix this, FedHelp uses special knowledge from other places to guide the learning process and share information. This helps small hospitals and clinics in poor areas make better diagnoses and provide better care. The results show that FedHelp works really well and makes a big difference for people who need it most. |
Keywords
» Artificial intelligence » Federated learning » Image classification » Knowledge distillation
