Multi-Continental Healthcare Modelling Using Blockchain-Enabled Federated Learning

TL;DR

This paper presents a blockchain-enabled federated learning framework for global healthcare modeling that preserves privacy, incentivizes honest participation, and achieves high prediction accuracy across multi-continental datasets.

Contribution

It introduces a novel federated learning approach integrated with blockchain technology to enable secure, privacy-preserving, and incentivized international healthcare data collaboration.

Findings

Framework is effective and privacy-preserving.

Achieves higher accuracy than limited local models.

Comparable or better than centralized training in some cases.

Abstract

One of the biggest challenges of building artificial intelligence (AI) model in the healthcare area is the data sharing. Since healthcare data is private, sensitive, and heterogeneous, collecting sufficient data for modelling is exhausting, 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) 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 adaptation to meet the privacy and safety requirements of healthcare data, meanwhile, it rewards honest participation and penalizes malicious activities using its on-chain incentive mechanism. Experimental results show that the proposed framework is effective, efficient, and privacy-preserving. Its prediction accuracy consistently outperforms models trained on limited personal data and achieves comparable or even slightly better results than centralized training in certain scenarios, all while preserving data privacy. This work paves the way for international collaborations on healthcare projects, where additional data is crucial for reducing bias and providing benefits to humanity.