Collaborative Deep Learning for Diabetic Retinopathy Diagnosis: A Federated Learning Approach

Early detection of diabetic retinopathy is necessary because it is the leading cause of blindness. For diagnosing diabetic retinopathy deep learning plays a significant role but there is challenge of privacy of patients data and various transactions. To address these issues, we propose a collaborative deep learning approach based on Federated Learning, where multiple clients can work in collaboration while training on data takes place at client side. In our system, deep learning model forwarded to clients and updations are forwarded back to global model. This ensures data privacy while benefiting from a diverse, multicenter dataset, which is crucial for enhancing model generalization across different demographics. We evaluate the performance of our federated learning approach in comparison to traditional centralized deep learning models, demonstrating its ability to achieve competitive accuracy levels without compromising data security. Furthermore, the system allows for scalable and continuous learning, where new data can be incorporated incrementally. Our results suggest that federated learning can be a promising framework for advancing DR diagnosis, making it feasible to build a robust, privacy-preserving diagnostic tool that can be deployed across various healthcare settings. This approach has the potential to revolutionize the detection and management of diabetic retinopathy globally while addressing critical challenges in healthcare data privacy.