(untitled) (International Journal of Engineering & Extended Technologies Research (IJEETR), 2026)
The rapid growth of healthcare data generated through electronic health records, medical imaging systems, wearable devices, and Internet of Medical Things (IoMT) platforms has created significant opportunities for advanced intelligence and predictive analytics. However, centralized cloud-based healthcare analytics face challenges related to data privacy, regulatory compliance, communication overhead, and scalability. Federated learning (FL) integrated with distributed cloud infrastructure provides an effective approach by enabling collaborative model training across multiple healthcare institutions while keeping sensitive patient data localized. This research explores optimization strategies for federated learning-enabled distributed cloud environments to enhance healthcare intelligence and predictive analytics performance. The study investigates resource allocation, communication efficiency, model aggregation techniques, security mechanisms, and adaptive cloud-edge collaboration frameworks. A comprehensive research methodology is proposed using simulation-based evaluation and performance benchmarking across healthcare datasets. Key performance indicators include prediction accuracy, latency, computational efficiency, privacy preservation, and scalability. The proposed framework aims to improve healthcare decision-making by enabling secure, intelligent, and efficient data-driven services. The integration of federated learning with distributed cloud architectures represents a transformative solution for personalized medicine, disease prediction, clinical decision support, and real-time healthcare analytics while maintaining patient confidentiality.