A Privacy-Preserving Collaborative Federated Learning Framework for Detecting Retinal Diseases

Abstract

The rapid advancement in technology has simplified human life and provides convenience. However, this convenience has led to many lifestyle diseases like diabetes and obesity. The 2021 reports of the International Diabetes Federation (IDF) show 537 million diabetics, three-fourths of whom are from developing countries. About 28.5% of these diabetics over 40 suffer from Diabetic Retinopathy (DR), 4.4% face vision-threatening DR, and 3.8% suffer from Diabetic Macular Edema (DME). These conditions can lead to complete vision loss affecting health and quality of life. Early detection of DR and DME is crucial to prevent harmful effects. The proposed work employs a collaborative, privacy-preserving Federated Deep Learning (FDL) framework with lightweight MobileNetV2 architecture for early detection of DR and DME. The proposed FDL framework uses both Independent and Identically Distributed (IID) and non-IID data for 2 and 3-client architectures. In a 2-client scenario, the FDL implementation with FedAvg aggregation achieved 98.69% accuracy on IID data and 87.09% on non-IID data, while FedProx aggregation scored 98.03% on IID data and 98.25% on non-IID data. In a 3-client scenario, FedAvg aggregation achieved 97.62% accuracy on IID data and 96.28% on non-IID data, whereas FedProx aggregation achieved 98.69% on IID data and 97.77% on non-IID data. The results demonstrate that the FedProx aggregation is more stable and converges earlier than FedAvg aggregation in the non-IID settings of an FDL framework. The proposed FDL framework with its collaborative training feature, preserves privacy and maintains high prediction accuracy.

Keywords

• Automatic disease detection
• early convergence
• federated deep learning framework
• healthcare
• machine learning
• MobileNetV2