U-FDL-PPE: a unified federated deep learning framework with privacy-preserving explainability for early and accurate viral disease prediction

Abstract

Introduction: Early and accurate detection of viral diseases is vital for timely treatment and public health preparedness. However, most existing computer-based prediction systems depend on centralized data storage, which raises concerns about patient privacy, compatibility between different hospitals, and limited clarity on how predictions are made. To address these issues, this study introduces U-FDL-PPE, a new federated deep-learning framework designed to support early and reliable viral disease diagnosis while protecting patient confidentiality and offering clear and understandable prediction insights. Methods: The framework uses a decentralized learning approach that allows hospitals to train models collaboratively without exchanging raw medical images. MobileNetV2 was used as the core model for classifying chest X-rays, and Grad-CAM was included to produce heatmaps that visually explain how the model arrived at its decisions. The system was tested using the publicly available COVID-19 Radiography Database in a simulated network of three healthcare institutions. Model performance was evaluated using standard measures such as accuracy, F1-score, AUC, and confusion matrix. Results: Across five training rounds, U-FDL-PPE recorded 88% accuracy, an F1-score of 89.66%, and a multi-class AUC of 0.5192. The confusion matrix showed consistently correct predictions across the three diagnostic categories: COVID-19, Normal, and Viral Pneumonia. The Grad-CAM heatmaps highlighted medically relevant lung regions, confirming that the framework focused on features that clinicians would expect when diagnosing these conditions. Discussion: The results indicate that U-FDL-PPE is a practical and scalable solution for early viral disease diagnosis, particularly in environments where patient privacy must be preserved. Its combination of decentralized training and visual explanation builds greater trust among clinicians while ensuring that sensitive medical data never leaves the originating institution. The lightweight MobileNetV2 architecture also supports faster processing, making the system suitable for hospitals and clinics with limited computing resources. Overall, U-FDL-PPE provides a privacy-conscious and transparent diagnostic framework that is well-positioned for real-world implementation across healthcare networks.