Sun, Rui;
Wang, Zhipeng;
Zhang, Hengrui;
Jiang, Ming;
Wen, Yizhe;
Sun, Jiahao;
Liu, Erwu;
(2025)
Multi-Continental Healthcare Modelling Using Blockchain-Enabled Federated Learning.
In:
2025 IEEE Global Blockchain Conference (GBC).
(pp. pp. 1-8).
IEEE: Shanghai, China.
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Abstract
One of the biggest challenges of building artificial intelligence (AI) model in healthcare area is the data sharing. Since healthcare data is private, sensitive, and heterogeneous, collecting sufficient data for modelling is exhausted, costly, and sometimes impossible. In this paper, we propose a framework for global healthcare modelling using datasets from multi-continents (Europe, North America and Asia) while without sharing the local datasets, and choose glucose management as a study model to verify its effectiveness. Technically, blockchain-enabled federated learning is implemented with adaption to make it meet with the privacy and safety requirements of healthcare data, meanwhile rewards honest participation and penalize malicious activities using its on-chain incentive mechanism. Experimental results show that the proposed framework is effective, efficient, and privacy preserved. Its prediction accuracy is much better than the models trained from limited personal data and is similar to, and even slightly better than, the results from a centralized dataset. This work paves the way for international collaborations on healthcare projects, where additional data is crucial for reducing bias and providing benefits to humanity.
| Type: | Proceedings paper |
|---|---|
| Title: | Multi-Continental Healthcare Modelling Using Blockchain-Enabled Federated Learning |
| Event: | 2025 IEEE Global Blockchain Conference (GBC) |
| Dates: | 20 Jun 2025 - 22 Jun 2025 |
| ISBN-13: | 979-8-3503-6642-6 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/GBC60041.2025.11134459 |
| Publisher version: | https://doi.org/10.1109/gbc60041.2025.11134459 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Federated learning, blockchain, glucose prediction, healthcare |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10215298 |
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