Federated Cloud Intelligence for Real-Time Precision Medicine
Keywords:
Cloud-native, federated intelligence, electronic health record, real-time analytics, precision medicineAbstract
Cloud-Native Federated Intelligence Framework for Real-Time EHR Analytics and AI-Driven Precision Medicine examined real-time electronic health record (EHR) analytics and artificial intelligence (AI)-driven precision medicine through dedicated applications based on a cloud-native federated intelligence framework. Real-world healthcare deployment requires cloud-native principles and technologies to meet strict demands for rapid response times, resilience, and security. Direct access to data between healthcare institutions presents significant privacy concerns. Federated Intelligence addresses these issues by defining a technology stack that ensures interoperability across diverse institutions and enabling a replicable, reusable, formally governed approach to safe data access.
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1. Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., Ourselin, S., Sheller, M., Summers, R. M., Trask, A., Xu, D., Baust, M., & Cardoso, M. J. (2020). The future of digital health with federated learning. NPJ Digital Medicine, 3, 119.
2. Sheller, M. J., Reina, G. A., Edwards, B., Martin, J., & Bakas, S. (2020). Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 105–115.
3. Kaissis, G. A., Makowski, M. R., Rückert, D., & Braren, R. F. (2020). Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence, 2, 305–311.
4. Kumar, M. V. K., Kolla, S. H., Pamisetty, V., Pandiri, L., Yandamuri, U. S., & Valiki, D. (2026). Enterprise-Scale Generative AI Agents for Secure and Governed Automation in Insurance and Public Financial Management. In 2026 International Conference on Computational Robotics, Testing and Engineering Evaluation (ICCRTEE) (pp. 1–6). IEEE. 2026 International Conference on Computational Robotics, Testing and Engineering Evaluation (ICCRTEE). https://doi.org/10.1109/iccrtee68719.2026.11566439
5. Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., Liu, A., Costa, A. B., Bernasconi, A., Bunce, C., Zeng, G., Jaffar, J., Koff, D., Kwak, S., Liu, X., Lucier, M., Maier-Hein, L., Maier-Hein, K., ... Li, Q. (2021). Federated learning for predicting clinical outcomes in patients with COVID-19. Nature Medicine, 27, 1735–1743.
6. Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology, 10(2), Article 12.
7. Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., D’Oliveira, A. B., Eichner, H., Rouayheb, S. E., Evans, D., Gardner, M., Garrett, Z., Gascón, A., Ghazi, B., Gibbons, P. B., ... Zhao, S. (2021). Advances and open problems in federated learning. Foundations and Trends in Machine Learning, 14(1–2), 1–210.
8. Kumar, S. S., Garapati, R. S., Segireddy, A. R., Kalisetty, S., Inala, R., & Nagabhyru, K. C. (2026). Hybrid Deep Neural Network–DevOps Pipeline Optimization for Risk Prediction in Cloud-Native Workers’ Compensation Platforms. In 2026 IEEE International Conference for Convergence in Computing Technology (I3CTCON) (pp. 1–6). IEEE. 2026 IEEE International Conference for Convergence in Computing Technology (I3CTCON). https://doi.org/10.1109/i3ctcon68242.2026.11507219
9. Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F. (2021). Federated learning for healthcare informatics. Journal of Healthcare Informatics Research, 5, 1–19.
10. Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., Ourselin, S., Sheller, M., Summers, R. M., Trask, A., Xu, D., Baust, M., & Cardoso, M. J. (2020). The future of digital health with federated learning. NPJ Digital Medicine, 3, 119.
11. Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50–60.
12. Thutari, R. T., Garapati, R. S., B M, Manjula., R K, Supriya., & M, Senbagan. (2025). Adaptive Access Control and Authentication Management for IoT Using Attention-GRU and Reinforcement Learning. In 2025 2nd International Conference on Software, Systems and Information Technology (SSITCON) (pp. 1–6). IEEE. 2025 2nd International Conference on Software, Systems and Information Technology (SSITCON). https://doi.org/10.1109/ssitcon66133.2025.11342003
13. Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2020). Federated learning: Strategies for improving communication efficiency. In Proceedings of the 2020 International Conference on Machine Learning.
14. McMahan, H. B., Ramage, D., Talwar, K., & Zhang, L. (2020). Learning differentially private recurrent language models. International Conference on Learning Representations.
15. Radha, S., Gottimukkala, V. R. R., Thottara, S., Vandhana, K., & J, Gokulraj. (2025). Adaptive Video Streaming Over 5G Networks Using Deep Reinforcement Learning with Closed-Loop Feedback Mechanism for Bitrate Control. In 2025 International Conference on Communication, Computer, and Information Technology (IC3IT) (pp. 1–6). IEEE. 2025 International Conference on Communication, Computer, and Information Technology (IC3IT). https://doi.org/10.1109/ic3it66137.2025.11341184
16. Kaissis, G. A., Ziller, A., Passerat-Palmbach, J., Ryffel, T., Usynin, D., Trask, A., Lima, I., Mancuso, I., Jungmann, F., Steinborn, M. M., Saleh, A., Rueckert, D., & Braren, R. F. (2021). End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nature Machine Intelligence, 3, 473–484.
17. Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., & Bakas, S. (2020). Federated learning in medicine: Facilitating multi-institutional collaborations without sharing patient data. Scientific Reports, 10, 12598.
18. Li, W., Milletari, F., Xu, D., Rieke, N., Hancox, J., Zhu, W., Baust, M., Cheng, Y., Ourselin, S., Cardoso, M. J., & Feng, A. (2020). Privacy-preserving federated brain tumour segmentation. In Medical Image Computing and Computer Assisted Intervention.
19. Reddy, M. S. R. L., Sunitha, T., Kanchana, K., Nagubandi, A. R., Segireddy, A. R., & Bhavanam, S. N. (2026). AI-Enhanced Blockchain Consensus Mechanisms for Secure Transaction Validation. In 2026 International Conference on Emerging Research in Smart Electronics and Machine Informatics (ECMI) (pp. 1–11). IEEE. 2026 International Conference on Emerging Research in Smart Electronics and Machine Informatics (ECMI). https://doi.org/10.1109/ecmi68341.2026.11602724
20. Yang, H., Zhao, H., Zhao, H., & Wang, X. (2021). Federated learning for medical imaging: A systematic review. Medical Image Analysis, 72, 102124.
21. Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F. (2021). Federated learning for healthcare informatics. Journal of Healthcare Informatics Research, 5, 1–19.
22. Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., Ourselin, S., Sheller, M., Summers, R. M., Trask, A., Xu, D., Baust, M., & Cardoso, M. J. (2020). The future of digital health with federated learning. NPJ Digital Medicine, 3, 119.
23. Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., Liu, A., Costa, A. B., Bernasconi, A., Bunce, C., Zeng, G., Jaffar, J., Koff, D., Kwak, S., Liu, X., Lucier, M., Maier-Hein, L., Maier-Hein, K., ... Li, Q. (2021). Federated learning for predicting clinical outcomes in patients with COVID-19. Nature Medicine, 27, 1735–1743.
24. Inala, R., Sheelam, G. K., Aitha, A. R., Lakshmi, A. U., Nagabhyru, K. C., & Segireddy, A. R. (2026). Architecting Hybrid Data Products Using AI/ML and Agentic AI for Group Insurance and Retirement Solution Platforms with Advanced Data Governance. In 2026 IEEE International Conference on AI Engineering and Innovations (AIEI) (pp. 1–6). IEEE. 2026 IEEE International Conference on AI Engineering and Innovations (AIEI). https://doi.org/10.1109/aiei69164.2026.11497971
25. Kaissis, G. A., Makowski, M. R., Rückert, D., & Braren, R. F. (2020). Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence, 2, 305–311.
26. Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., Niyato, D., & Poor, H. V. (2021). Federated learning for Internet of Things: A comprehensive survey. IEEE Communications Surveys & Tutorials, 23(3), 1622–1658.
27. Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., D’Oliveira, A. B., Eichner, H., Rouayheb, S. E., Evans, D., Gardner, M., Garrett, Z., Gascón, A., Ghazi, B., Gibbons, P. B., ... Zhao, S. (2021). Advances and open problems in federated learning. Foundations and Trends in Machine Learning, 14(1–2), 1–210.
28. Kumar, S. S., Garapati, R. S., Segireddy, A. R., Kalisetty, S., Inala, R., & Nagabhyru, K. C. (2026). Hybrid Deep Neural Network–DevOps Pipeline Optimization for Risk Prediction in Cloud-Native Workers’ Compensation Platforms. In 2026 IEEE International Conference for Convergence in Computing Technology (I3CTCON) (pp. 1–6). IEEE. 2026 IEEE International Conference for Convergence in Computing Technology (I3CTCON). https://doi.org/10.1109/i3ctcon68242.2026.11507219
29. Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50–60.
30. Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology, 10(2), Article 12.
31. Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., Liu, X., & He, B. (2021). A survey on federated learning systems: Vision, hype and reality for data privacy and protection. IEEE Transactions on Knowledge and Data Engineering, 35(4), 3347–3366.
32. Danghi, P. S., Maniraj, K., Jain, P., Adilakshmi, K., Garapati, R. S., & Jain, S. K. (2025). Artificial Intelligence Based Energy Optimization Framework for Wireless Sensor Networks. In 2025 IEEE 5th International Conference on ICT in Business Industry & Government (ICTBIG) (pp. 1–6). IEEE. 2025 IEEE 5th International Conference on ICT in Business Industry & Government (ICTBIG). https://doi.org/10.1109/ictbig68706.2025.11323860
33. Xu, J., Wang, H., & Wang, F. (2022). Federated learning for healthcare: A systematic review. Journal of Medical Internet Research, 24, e36492.
34. Joshi, M., Pal, A., & Sankarasubbu, M. (2022). Federated learning for healthcare domain—Pipeline, applications and challenges. ACM Transactions on Computing for Healthcare, 3(4), Article 30.
35. Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., Ourselin, S., Sheller, M., Summers, R. M., Trask, A., Xu, D., Baust, M., & Cardoso, M. J. (2020). The future of digital health with federated learning. NPJ Digital Medicine, 3, 119.
36. Baladari, V., Nagubandi, A. R., Charan Teja Tadi, S. R. C., Selvi, A. T., Sreedevi, V., & Nithya, M. (2026). Predictive AI Model for Financial Risk Assessment in Dynamic Market Environments. In 2026 International Conference on Communication, Computing and Emerging Technologies (IC3ET) (pp. 748–753). IEEE. 2026 International Conference on Communication, Computing and Emerging Technologies (IC3ET). https://doi.org/10.1109/ic3et64989.2026.11467452
37. Li, S., Liu, P., Nascimento, G. G., Wang, X., Leite, F. R. M., Chakraborty, B., Hong, C., Ning, Y., Xie, F., Teo, Z. L., Ting, D. S. W., Haddadi, H., Ong, M. E. H., Peres, M. A., & Liu, N. (2023). Federated and distributed learning applications for electronic health records and structured medical data: A scoping review. Journal of Biomedical Informatics, 144, 104427.
38. Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., Liu, A., Costa, A. B., Bernasconi, A., Bunce, C., Zeng, G., Jaffar, J., Koff, D., Kwak, S., Liu, X., Lucier, M., Maier-Hein, L., Maier-Hein, K., ... Li, Q. (2021). Federated learning for predicting clinical outcomes in patients with COVID-19. Nature Medicine, 27, 1735–1743.
39. Inala, Ramesh, Ravi Shankar Garapati, Avinash Reddy Aitha, Venkata Bhardwaj Komaragiri, Vijaya Rama Raju Gottimukkala, Mahesh Recharla, Pallav Kumar Kaulwar et al. "Ai-driven adaptive meta-learning framework for self-evolving neural architecture optimization." U.S. Patent Application 19/389,108, filed March 12, 2026.
40. Feng, Y., Thapa, S., & others. (2022). Federated learning for healthcare applications: A survey. ACM Computing Surveys, 55(4), Article 78.
41. Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., Niyato, D., & Poor, H. V. (2022). Federated learning for Internet of Things: A comprehensive survey. IEEE Communications Surveys & Tutorials, 24(1), 1–39.
42. Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., & Bakas, S. (2020). Federated learning in medicine: Facilitating multi-institutional collaborations without sharing patient data. Scientific Reports, 10, 12598.
43. Garapati, R. S., Paleti, S., Meda, R., Nagabhyru, K. C., & Deepa Priya, B. S. (2026). Physical-Unclonable-Function-Based Secure and Anonymous User Authentication for Smart Homes. In Lecture Notes in Electrical Engineering (pp. 367–378). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-20235-2_33
44. Yang, H., Miao, Y., Liu, Y., & others. (2022). Federated learning for healthcare: A review of applications, challenges, and future directions. Artificial Intelligence Review, 55, 1–35.
45. Li, W., Milletari, F., Xu, D., Rieke, N., Hancox, J., Zhu, W., Baust, M., Cheng, Y., Ourselin, S., Cardoso, M. J., & Feng, A. (2020). Privacy-preserving federated brain tumour segmentation. In Medical Image Computing and Computer Assisted Intervention.
46. Kaissis, G. A., Makowski, M. R., Rückert, D., & Braren, R. F. (2020). Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence, 2, 305–311.
47. Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., Liu, A., Costa, A. B., Bernasconi, A., Bunce, C., Zeng, G., Jaffar, J., Koff, D., Kwak, S., Liu, X., Lucier, M., Maier-Hein, L., Maier-Hein, K., ... Li, Q. (2021). Federated learning for predicting clinical outcomes in patients with COVID-19. Nature Medicine, 27, 1735–1743.
48. Sheller, M. J., Reina, G. A., Edwards, B., Martin, J., & Bakas, S. (2020). Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 105–115
49. Kalita, T., Prasad Tiwari, S., Reddy Aitha, A., & Garg, A. (2026). Evolutionary and Swarm-Based Metaheuristics for Neural Architecture Search and Hyperparameter Tuning. Available at SSRN 6708638.
50. Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F. (2021). Federated learning for healthcare informatics. Journal of Healthcare Informatics Research, 5, 1–19.
51. Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50–60.
52. Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., Liu, X., & He, B. (2021). A survey on federated learning systems: Vision, hype and reality for data privacy and protection. IEEE Transactions on Knowledge and Data Engineering, 35(4), 3347–3366.
53. Li, S., Liu, P., Nascimento, G. G., Wang, X., Leite, F. R. M., Chakraborty, B., Hong, C., Ning, Y., Xie, F., Teo, Z. L., Ting, D. S. W., Haddadi, H., Ong, M. E. H., Peres, M. A., & Liu, N. (2023). Federated and distributed learning applications for electronic health records and structured medical data: A scoping review. Journal of Biomedical Informatics, 144, 104427.
54. Wu, C., Wu, S., & others. (2022). Federated learning for medical applications: A systematic review. IEEE Reviews in Biomedical Engineering, 15, 1–15.
55. Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., & Gao, Y. (2021). A survey on federated learning. Knowledge-Based Systems, 216, 106775.
56. Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F. (2021). Federated learning for healthcare informatics. Journal of Healthcare Informatics Research, 5, 1–19.
57. Davuluri, P. S. L. (2023). AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems (December 15, 2023).
58. Antunes, R. S., André da Costa, C., Küderle, A., Yari, I. A., & Eskofier, B. M. (2022). Federated learning for healthcare: Systematic review and architecture proposal. ACM Transactions on Computing for Healthcare, 3(2), Article 18.
59. Pati, S., Baid, U., Edwards, B., Sheller, M. J., Wang, S.-H., Reina, G. A., Foley, W. D., Milletari, F., & Bakas, S. (2022). Federated learning enables big data for rare cancer boundary detection. Nature Communications, 13, 7346.
60. Stripelis, D., Saleem, H., Dhinagar, N., & others. (2022). Federated learning of deep learning models for medical applications. Journal of Biomedical Informatics, 132, 104099.
61. Feki, I., Ammar, S., Kessentini, Y., & Muhammad, K. (2021). Federated learning for COVID-19 screening from chest X-ray images. Pattern Recognition, 121, 108263.
62. Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., Ourselin, S., Sheller, M., Summers, R. M., Trask, A., Xu, D., Baust, M., & Cardoso, M. J. (2020). The future of digital health with federated learning. NPJ Digital Medicine, 3, 119.
63. Bakas, S., Li, X., Shah, P., & Roth, H. R. (2026). Federated learning in healthcare: From research to real-world deployment. Annual Review of Biomedical Engineering, 28, 163–186.
64. Empowering precision medicine: Unlocking revolutionary insights through blockchain-enabled federated learning and electronic medical records. (2023). Sensors, 23(17), 7476.
65. Federated learning: Breaking down barriers in global genomic research. (2025). Journal of Personalized Medicine, 15.
66. From challenges and pitfalls to recommendations and opportunities: Implementing federated learning in healthcare. (2025). Journal of Biomedical Informatics, 162.
67. Federated learning in healthcare: Recent progress and challenges. (2026). Computers and Electrical Engineering, 131, 110924.
68. From algorithmic innovation to clinical deployment: A systematic review of methodological gaps limiting federated learning in healthcare. (2026). International Journal of Medical Informatics, 213, 106365.
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