DIABETIC RETINOPATHY DETECTION USING FEDERATED LEARNING AND VISION TRANSFORMERS

Early detection of Diabetic retinopathy (DR) is essential for preventing blindness because this disease currently stands as the primary cause of blindness. The implementation of traditional deep learning models becomes difficult because data privacy risks alongside insufficient dataset availability among different healthcare institutions. We introduce an FL-ViT cooperative model training framework which supports distributed information processing without requiring shared clinical data ownership. The use of ViTs in self-attention feature extraction from retinal images together with FL technology guarantees privacy standards compliance. Healthcare operators use a Model designed for the sector to produce diagnostic documents that speed up clinical operations. The system achieves 93 percentage accuracy, ROC curve is 0.89 , precision and recall and confusion matrix in DR grading according to APTOS dataset evaluations. The solution resolves issues pertaining to AI scalability and generalizability along with compliance with healthcare ethical requirements.