Privacy-Preserving and Efficient Pneumonia Diseases Detection System Based on Federal Intelligent Edges

Abstract

As pneumonia cases continue to rise worldwide, rapid diagnostic capabilities are essential for effective treatment. However, traditional medical systems often lack efficiency and coordinated management. In response, we propose an AI-driven biomedical diagnosis platform for real-time detection and swift intervention. Leveraging privacy-preserving deep learning on the edge, users can promptly obtain automated diagnoses by uploading chest CT images. To further enhance accuracy, we employ a federated learning (FL) framework that ensures scalable training in an industrial IoT setting while protecting patient data. Our global FL model achieves around 96.25% accuracy on a validation dataset, outperforming individual clients by 3.42%. By eliminating the need for sharing raw data, patient privacy is preserved, and the system offers improved flexibility and scalability for medical diagnosis.