Diabetic Retinopathy (DR) is the primary cause of preventable blindness among people with diabetes where early diagnosis is important. It is required to avoid permanent loss of vision impairment. Traditional deep learning-based diagnostic models often depend on centralized repositories of medical images which raises critical concerns about patient privacy, regulatory compliance, and institutional data ownership. Considering these challenges this work presents a Federated Learning (FL) framework for multi-class DR detection, that enables multiple healthcare institutions to collaboratively train a single model without sharing the raw patient data. In the proposed system Gaussian-filtered retinal fundus images resized to 224×224 are used to train local deep learning models across distributed clients. We conduct a comparative analysis through a Custom CNN, MobileNetV2, VGG16, and ResNet50 architectures by only the optimized model parameters are transmitted to a Central server for aggregation using the federated averaging algorithm (FedAvg). Experimental evaluation shows that the ResNet50 architecture achieves the highest diagnostic accuracy (78%), closely tagged by MobileNetV2 (76%), showing that deeper and more robust feature extractors maintain performance superiority even under non-IID FL constraints. The federated model that not only preserves privacy and confidentiality but also achieves clinically reliable diagnostic accuracy with improved generalization across diverse datasets. These results underscore the potential of Federated Learning as secure, scalable, and privacy-preserving solution for early DR screening and real-world large-scale healthcare deployment.