Summary of Multi-continental Healthcare Modelling Using Blockchain-enabled Federated Learning, by Rui Sun et al.
Multi-Continental Healthcare Modelling Using Blockchain-Enabled Federated Learning
by Rui Sun, Zhipeng Wang, Hengrui Zhang, Ming Jiang, Yizhe Wen, Jiqun Zhang, Jiahao Sun, Shuoying Zhang, Erwu Liu, Kezhi Li
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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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 proposed framework for global healthcare modeling utilizes blockchain-enabled federated learning to collect data from multiple continents without sharing local datasets. The approach is tested using glucose management as a study model, demonstrating improved prediction accuracy compared to models trained on limited personal data. The framework’s efficiency and privacy preservation are also highlighted. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers address the challenge of collecting healthcare data for AI modeling by proposing a new method. They use blockchain technology to combine data from different parts of the world without sharing sensitive information. This helps reduce bias in medical research and improves predictions. The approach is tested with glucose management data and shows promising results. |
Keywords
» Artificial intelligence » Federated learning
