JMIR Publications

ABSTRACT

Background:

Diabetes prediction requires accurate, privacy-preserving, and scalable solutions. Traditional machine learning models rely on centralized data, posing risks to data privacy and regulatory compliance. Moreover, healthcare settings are highly heterogeneous, with diverse participants, hospitals, clinics, and wearables, producing non-IID data and operating under varied computational constraints. Learning in isolation at individual institutions limits model generalizability and effectiveness. Collaborative federated learning enables institutions to jointly train models without sharing raw data, but current approaches often struggle with heterogeneity, security threats, and system coordination.

Objective:

This study aims to develop a secure, scalable, and privacy-preserving framework for diabetes prediction by integrating federated learning with ensemble modeling, blockchain-based access control, and knowledge distillation. The framework is designed to handle data heterogeneity, non-IID distributions, and varying computational capacities across diverse healthcare participants, while simultaneously enhancing data privacy, security, and trust.

Methods:

We propose a Federated Ensemble Learning framework, FedEnTrust, that enables decentralized healthcare participants to collaboratively train models without sharing raw data. Each participant shares soft label outputs, which are distilled and aggregated through adaptive weighted voting to form a global consensus. The framework supports heterogeneous participants by assigning model architectures based on local computational capacity. To ensure secure and transparent coordination, a blockchain-enabled smart contract governs participant registration, role assignment, and model submission with strict role-based access control. We evaluated the system on the PIMA Indians Diabetes Dataset, measuring prediction accuracy, communication efficiency, and blockchain performance.

Results:

The FedEnTrust framework achieved 84.2% accuracy, with precision, recall, and F1-score of 84.6%, 88.6%, and 86.4%, respectively, outperforming existing decentralized models and nearing centralized deep learning benchmarks. The blockchain-based smart contract ensured 100% success for authorized transactions and rejected all unauthorized attempts, including malicious submissions. Average blockchain latency was 210 milliseconds, with a gas cost of ~107,940 units, enabling secure, real-time interaction. Throughout, patient privacy was preserved by exchanging only model metadata, not raw data.

Conclusions:

FedEnTrust offers a deployable, privacy-preserving solution for decentralized healthcare prediction by integrating federated learning, ensemble modeling, blockchain-based access control, and knowledge distillation. It balances accuracy, scalability, and ethical data use while enhancing security and trust. This work demonstrates that secure federated ensemble systems can serve as practical alternatives to centralized AI models in real-world healthcare applications.