Nowadays, the digitalisation of healthcare has, in turn, generated outstanding volumes of heterogeneous data from EHRs, IoMT devices, and telemedicine platforms, requiring secure and scalable analytical frameworks. Existing monolithic systems now face issues related to scalability, interoperability, and compliance while also putting patient privacy at risk. Our study describes a new federated microservices architecture that integrates Kubernetes-orchestrated microservices, TensorFlow Federated learning, and Hyperledger Fabric blockchain to enable privacy-preserving, scalable, and auditable analytics in healthcare. In contrast to prior works focusing on isolated solutions, our framework presents an end-to-end deployable system with modular scalability, differential privacy, and immutable auditability. We have evaluated the framework on 100, 000 synthetic Synthea records and a real-world dataset of 20, 000 diabetes patients. The framework achieved 95. 2% predictive accuracy, 42% lower latency, and 10 \ (\) faster recovery than the monolithic baselines while ensuring zero breach success in adversarial simulations. These results demonstrate that the proposed architecture not only improves clinical decision support accuracy but also provides operational resilience, regulatory compliance, and cost efficiency. This work lays the foundation for next-generation intelligent healthcare systems, with future extensions toward multimodal data and explainable AI to enhance trust and adoption in clinical practice.
Harshith et al. (2026) studied this question.