FQPDR: Federated Quantum Neural Network for Privacy-preserving Early Detection of Diabetic Retinopathy

Researchers propose FQPDR, a federated quantum neural network system for early detection of diabetic retinopathy that preserves patient privacy by processing medical data locally rather than centralizing it. The approach combines federated learning with quantum computing to identify microaneurysm dots—the earliest signs of diabetic retinopathy—while maintaining data confidentiality across distributed healthcare systems.

FQPDR represents an intersection of three critical healthcare technology domains: quantum computing, federated learning, and medical diagnostics. The system addresses a genuine clinical challenge—early diabetic retinopathy detection prevents blindness—while solving a persistent healthcare IT problem: how to leverage distributed patient data without centralizing sensitive medical records. Federated learning enables hospitals and clinics to train collaborative models by sharing only algorithmic parameters rather than actual patient images, a substantial privacy advantage over traditional centralized approaches.

The incorporation of quantum neural networks introduces computational advantages for pattern recognition in complex medical imaging. Microaneurysms present genuine detection difficulty due to their small size and low contrast; quantum approaches may offer improved efficiency in processing these subtle features compared to classical algorithms. Testing on multiple datasets (E-ophtha, Retina MNIST, and Kaggle) demonstrates the system's robustness across different data sources.

For the healthcare technology sector, this research validates the feasibility of privacy-preserving machine learning at clinical scale. The lightweight architecture—using limited samples and parameters—suggests deployability in resource-constrained healthcare environments, particularly important in developing regions with high diabetes prevalence. The federated framework allows institutions to maintain regulatory compliance while benefiting from collaborative intelligence.

Key challenges remain: quantum hardware accessibility, integration with existing hospital infrastructure, and clinical validation across larger patient populations. The importance of this work lies not in immediate market disruption but in establishing technical foundations for privacy-respecting medical AI systems, a regulatory imperative as healthcare institutions face increasing data protection requirements.

• →Federated quantum neural networks enable early diabetic retinopathy detection while preserving patient data privacy by processing information locally rather than centralizing it.
• →The lightweight model architecture with limited parameters suggests deployment feasibility in resource-constrained healthcare environments globally.
• →Quantum computing's pattern recognition advantages appear particularly suited to detecting subtle medical anomalies like microaneurysms with low contrast.
• →The approach addresses growing regulatory pressure on healthcare institutions to implement privacy-by-design AI systems compliant with GDPR and similar frameworks.
• →Clinical validation across multiple datasets demonstrates robustness, though real-world hospital deployment remains an unvalidated next step.