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

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.