A Federated Learning Approach for Diabetic Retinopathy Detection Using FedAvg and FedProx

ABSTRACT

Diabetic Retinopathy (DR) poses a significant global health challenge, often leading to vision impairment if not detected early. Traditional centralized machine learning approaches for DR screening rely on collecting sensitive medical data in one place, which often raises serious privacy concerns. To address this, we explore Federated Learning (FL) paradigms that enable collaborative model training across distributed devices without sharing raw data. This study implements and compares two FL algorithms: Federated Averaging (FedAvg) and Federated Proximal (FedProx), tailored for binary DR classification using retinal fundus images. Our approach utilizes a convolutional neural network (CNN) backbone in a simulated FL environment with five clients.These findings highlight FedProx's robustness in handling client heterogeneity, offering a privacy-preserving solution for scalable DR detection in healthcare settings.

Keywords — Federated Learning, Diabetic Retinopathy, FedAvg, FedProx, Privacy-Preserving AI, Deep Learning in Healthcare.