Advanced Privacy-Preserving AI Techniques for Distributed Disease Diagnosis

As healthcare AI systems grow increasingly data-driven, preserving patient privacy while maintaining diagnostic accuracy has become a critical challenge. Traditional centralized training models often conflict with HIPAA and GDPR regulations by requiring sensitive patient data to be pooled. Federated Learning (FL) is an innovative technique that allows multiple institutions to jointly train models while keeping their raw data decentralized and private. This paper explores the theoretical foundation of FL, its integration with privacy-preserving technologies, and its practical applications in disease diagnosis, including COVID-19, diabetic retinopathy, and cancer detection. We propose a Hybrid Federated Learning (HFL) framework that combines Differential Privacy (DP) and Homomorphic Encryption (HE) for secure, scalable deployment in clinical settings. Evaluating the framework on a simulated multi-hospital chest X-ray dataset (3,900 images), the proposed HFL-PDD model achieved an average accuracy of 92.5% and a recall of 93.2%. Quantitative privacy leakage analysis showed less than 1% risk of patient re-identification, far below thresholds observed in non-federated approaches. The integration of Explainable AI (XAI) modules further ensures clinical interpretability, making the system suitable for real-world diagnostic assistance. These findings highlight the practical potential of FL in transforming healthcare applications while maintaining absolute patient confidentiality.