Privacy preservation in diabetic disease prediction using federated learning based on efficient cross stage recurrent model

Abstract

Diabetic retinopathy (DR) is a major problemfor the diabetes patients that makes a serious threat to vision and causes the irreversible blindness if not diagnosed and treated early. Conventional deep learning-based approaches designed for DR detection have demonstrated promising results; still, the requirement of centralized data aggregation makes privacy and security concerns for sharing the healthcare data. Thus, for providing the privacy preservation federated learning (FL) based methods were designed; still, the computation overhead and inaccurate detection of disease limits the performance. Hence, this research introduces a privacy-preserving framework named federated learning based diabetic retinopathy detection network (FedDRNet) model. The proposed FedDRNet model includes efficient cross stage recurrent network (ECSRNet) for training the local and server model that combines the strengths of ShuffleNet, CSPNet, and GRU to achieve high accuracy and computational efficiency. Besides, to strengthen the privacy, Homomorphic Encryption is applied prior to the update sharing for obtaining secure communication between clients and the central server. Also, improved K-means clustering (IKMC) based user selection enhances the communication efficiency by reducing the communication rounds. The analysis of FedDRNet by implementing in PYTHON programming tool based on Accuracy, Precision, Recall, F-Score, and Specificity acquired the values of 98.6, 98.8, 98.3, 98.6, and 98.1% respectively.

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