Federated Deep Learning Framework for Efficient Detection of Diabetic Retinopathy

One of the major factors in causing vision loss and blindness in people who have been diagonalized with Diabetes is Diabetic Retinopathy (DR). An early diagnosis is always helpful in effective treatment. This research proposes a framework based on federated learning that involves a combination of MobileNetV2 and ResNet50 architectures to detect and classify DR. By using this method, the privacy of data is also increased. To overcome class imbalance, the proposed method applies a weighted loss function, PCA allows dimensionality reduction and data is augmented to develop a stronger model. Using federated learning, the framework enables distributed training of models on separate data, while still maintaining patient confidentiality and good accuracy. Research suggests that bringing federated deep learning into hospitals can create practical, secure and scalable ways to address problems in medical imaging. This work lightens the obstacles that exist in spotting diabetic retinopathy, including gathering lots of annotated data, using complex deep learning methods and embedding these methods in day-to-day medical practices. A ResNet50 deep learning model achieves 4.35% higher precision, 12.9% better recall, 15.94% greater F1-score, 20.97% better accuracy and 2.67% greater AUC-ROC when running federated learning than mobile deep learning models like MobileNetV2. Existing patterns and possible future directions are then discussed, with an emphasis on finding methods to improve the diagnosis of Diabetic Retinopathy.