Abstract
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 \(\times\) 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.
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Data availability
The datasets analysed in this study include both real-world and synthetic healthcare data. The real-world Type 2 diabetes dataset was obtained from the publicly available UCI Machine Learning Repository (Diabetes 130-US hospitals for the years 1999–2008), which is fully de-identified and accessible at https://archive.ics.uci.edu/dataset/296/diabetes+130-us+hospitals+for+years+1999-2008. As a publicly available, de-identified dataset, its use qualifies for exemption under 45 CFR 46.104(d)(4), and all data handling complies with HIPAA Safe Harbor de-identification standards and applicable GDPR requirements. In addition, synthetic patient records were generated using the open-source Synthea™ platform, available at https://synthetichealth.github.io/synthea, to support controlled experimentation. All datasets were processed and stored in accordance with relevant data protection regulations. The processed datasets and model implementation scripts are available from the corresponding author upon reasonable request for academic and non-commercial research purposes.
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Funding
Open access funding provided by Symbiosis International (Deemed University). This work is supported by the Research Support Fund (RSF) of Symbiosis International (Deemed University), Pune, India.
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Z.A.A. conceived the study and designed the overall federated microservices framework. M.H. and Z.A.A. implemented the system architecture, developed the containerized microservices, and conducted the experiments, including federated model training and performance evaluation. S.S. (Sreyan Swarna) and D.R.N. contributed to data preprocessing, result interpretation, and performance analysis. S.F. and S.W.M. assisted in manuscript drafting, literature validation, and the refinement of experimental design. Shadab Siddiqui supported visualisation, figure preparation, and blockchain integration validation. All authors reviewed, edited, and approved the final version of the manuscript.
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Harshith, M., Ansari, Z.A., Fatima, S. et al. Federated microservices architecture with blockchain for privacy-preserving and scalable healthcare analytics. Sci Rep (2026). https://doi.org/10.1038/s41598-026-39837-1
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DOI: https://doi.org/10.1038/s41598-026-39837-1
