Federated AI and Big Data Architectures for Global Healthcare Collaboration
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I6P111Keywords:
Federated Learning in Healthcare, Federated Reinforcement Learning, Big Data Architectures for Health, Cloud of Trust Infrastructure, Healthcare Data Lakes and Data Hubs, Semantic Data Integration Standards, Open Data Exchange Protocols, Privacy-Preserving AI Systems, Data Sovereignty Frameworks, Cross-Organizational Federated Computing, Pandemic Surveillance Analytics, Precision Medicine Platforms, Genomic Data Collaboration, Public Cloud AI for Healthcare, Elastic Self-Service Data Infrastructure, Open-Source Federated AI Libraries, Cross-Functional Collaboration Models, FR-FA-CFCM Governance Framework, Distributed Healthcare Intelligence Systems, Global Health Data EcosystemsAbstract
Advances in Big Data architectures and AI techniques based on Federated Learning improve the opportunities for collaboration in healthcare for use cases such as pandemic surveillance and precision medicine. Big Data infrastructures have recently evolved from classical data warehouses to include data lakes and data hubs. Fleets of data lakes and data hubs form a cloud of trust, enabling federated computing across organization boundaries. These concepts provide the foundations for a Big Data architecture for global healthcare based on semantically compliant integration of data hubs and lakes using open data exchange and description standards created by the international community. Recent developments in open-source software libraries for Federated Learning support data protection and data sovereignty with minimal data exposure to other organizations. Proposed solutions combine the benefits of Federated Learning with the elastic, self-service, and pay-as-you-go aspects of the public cloud for AI in healthcare.Future developments target global health, with specific applications to pandemic surveillance and response, and to precision medicine and genomics. The evolution of architectures for AI in health, based on Federated Learning and Federated Reinforcement Learning, is guided by the cross-functional collaboration model FR-FA-CFCM. The model formalizes the rules, roles, and mechanisms for co-creating software solutions among different stakeholder roles across organization boundaries, on-premise and in the cloud.
References
[1] NIST. (2023). Artificial intelligence risk management framework (AI RMF 1.0). National Institute of Standards and Technology.
[2] Kolla, S. K. (2021). Designing Scalable Healthcare Data Pipelines for Multi-Hospital Networks. World Journal of Clinical Medicine Research, 1(1), 1–14. Retrieved from https://www.scipublications.com/journal/index.php/wjcmr/article/view/1376
[3] Armbrust, M., Zaharia, M., Xin, R. S., et al. (2015). Apache Spark: A unified engine for big data processing. Communications of the ACM, 59(11), 56–65.
[4] Varri, D. B. S. (2024). Adaptive and Autonomous Security Frameworks Using Generative AI for Cloud Ecosystems. Available at SSRN 5774785.
[5] Batini, C., & Scannapieco, M. (2016). Data and information quality: Dimensions, principles and techniques. Springer.
[6] Babaiah, C., Dobriyal, N., Shamila, M., Aitha, A. R., Patel, S. P., & Upodhyay, D. (2025, December). Intelligent Fault Detection and Recovery in Wireless Sensor Networks Using AI. In 2025 IEEE 5th International Conference on ICT in Business Industry & Government (ICTBIG) (pp. 1-6). IEEE.
[7] Benjamens, S., Dhunnoo, P., & Meskó, B. (2020). The state of artificial intelligence-based FDA-approved medical devices. NPJ Digital Medicine, 3, 118.
[8] Davuluri, P. N. Integrating Artificial Intelligence into Event-Driven Financial Crime Compliance Platforms.
[9] Bertsekas, D. P. (2012). Dynamic programming and optimal control (Vol. 1). Athena Scientific.
[10] Vajpayee, A., Khan, S., Gottimukkala, V. R. R., Sharma, D., & Seshasai, S. J. (2025). Digital Financial Literacy 4.0: Consumer Readiness for AI-Driven Fintech and Blockchain Ecosystems. International Insurance Law Review, 33(S5), 963-973.
[11] Brundage, M., Avin, S., Clark, J., et al. (2018). The malicious use of artificial intelligence. arXiv.
[12] Nigam, N., Sireesha, B., Ediga, P., Segireddy, A. R., & Bokde, S. (2025, December). Comparative Evaluation of Cloud Security Algorithms Using Multiple Classifiers with an Optimized Intrusion Detection System. In 2025 IEEE 5th International Conference on ICT in Business Industry & Government (ICTBIG) (pp. 1-6). IEEE.
[13] Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19, 171–209.
[14] Pareyani, S., Goswami, S., Geetha, Y., Dimri, S. K., Niharika, D. S., & Amistapuram, K. (2025, December). Smart Resource Allocation in Wireless Sensor Networks Through AI Techniques. In 2025 IEEE 5th International Conference on ICT in Business Industry & Government (ICTBIG) (pp. 1-6). IEEE.
[15] Vijaya Rama Raju Gottimukkala. (2025). Agentic AI for Next-Generation Cross-Border Payments: Contextual Learning in Transaction Routing. Journal of Informatics Education and Research, 5(4). Retrieved from https://jier.org/index.php/journal/article/view/3794
[16] Varri, D. B. S. V. (2025). Human-AI collaboration in healthcare security.
[17] Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science, 9(3–4), 211–407.
[18] Nagubandi, A. R. (2025). Cryptocurrency Market Spillovers: Risk Contagion Across Global Financial Systems.
[19] European Parliament and Council of the European Union. (2016). General Data Protection Regulation (GDPR). Official Journal of the European Union.
[20] Yandamuri, U. S. AI-Driven Decision Support Systems for Operational Optimization in Hospitality Technology.
[21] Gentry, C. (2009). A fully homomorphic encryption scheme. Stanford University.
[22] Guntupalli, R. (2025). Federated Deep Learning for Predictive Healthcare: A Privacy-Preserving AI Framework on Cloud-Native Infrastructure. Vascular and Endovascular Review, 8(16s), 200-210.
[23] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
[24] Dutta, P., Mondal, A., Vadisetty, R., Polamarasetti, A., Guntupalli, R., & Rongali, S. K. (2025). A novel deep learning rule-based spike neural network (SNN) classification approach for diagnosis of intracranial tumors. International Journal of Information Technology, 17(9), 5705-5712.
[25] [25] He, J., Baxter, S., Xu, J., et al. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25, 30–36.
[26] [26] Enterprise-Scale Gen AI Orchestration Using Small LMs and LLM Agents for Intelligent ITSM and HRSD Automation in Enterprise Ecosystems. (2025). MSW Management Journal, 35(2), 1889-1897.
[27] Holzinger, A. (2016). Interactive machine learning for health informatics. Springer.
[28] FinOps Strategies for AI-Enabled Real-Time Compliance Platforms in Cloud Native Environments. (2025). MSW Management Journal, 35(2), 2080-2088.
[29] IBM. (2023). Data fabric architecture overview. IBM Redbooks.
[30] Yandamuri, U. S. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. International Journal Of Finance, 36(6), 682-706.
[31] Sasi Kumar Kolla. (2023). Big Data–Driven Machine Learning Frameworks for Clinical Risk Prediction. International Journal of Medical Toxicology and Legal Medicine, 26(3 and 4), 44–59. Retrieved from https://ijmtlm.org/index.php/journal/article/view/1456
[32] Kelly, C. J., Karthikesalingam, A., Suleyman, M., et al. (2019). Key challenges for delivering clinical impact with AI. BMC Medicine, 17, 195.
[33] Kumar, K. M., Parasar, A., Walia, A., Inala, R., & Thulasimani, T. (2025, August). Enhancing Risk Management Strategies in Financial Institutions Using CNN and Support Vector Regression. In 2025 5th Asian Conference on Innovation in Technology (ASIANCON) (pp. 1-6). IEEE.
[34] Koller, D., & Friedman, N. (2009). Probabilistic graphical models. MIT Press.
[35] Rao, A. N., Garapati, R. S., Suganya, R. T., Kaliappan, A., & Kamaleshwar, T. (2025, August). Smart Solar Harvesting and Power Management in IoT Nodes Through Deep Learning Models. In 2025 2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) (pp. 1-6). IEEE.
[36] Liu, F., et al. (2025). Foundational architecture for AI agents in healthcare. Cell Reports Medicine, 6(10), 102374.
[37] Paleti, S., Baliyan, M., Aitha, A. R., Reddy, B. A., Bhadauria, G. S., & Sing, S. A. (2025, August). Graph—LSTM Hybrid Model for Improving Fraud Detection Accuracy in E-Commerce Financial Services. In 2025 2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) (pp. 1-6). IEEE.
[38] Moreau, L., & Groth, P. (2013). Provenance: An introduction to PROV. Morgan & Claypool.
[39] Nagabhyru, K. C., Rani, M., Reddy, D. S., & Krishnaraj, V. (2025, August). Machine Learning-Driven Fault Detection in Electric Vehicles via Hybrid Reinforcement Learning Model. In 2025 2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) (pp. 1-6). IEEE.
[40] Obermeyer, Z., & Emanuel, E. (2016). Predicting the future—Big data and clinical medicine. NEJM, 375, 1216–1219.
[41] Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning. ACM Transactions on Intelligent Systems and Technology, 10(2), 1–19.
[42] Pearl, J. (2009). Causality (2nd ed.). Cambridge University Press.
[43] Srikanth, T., Segireddy, A. R., & Elavarasi, S. A. (2025, October). STaSFormer-SGAD: Semantic Triplet-Aware Spatial Flow-Guided Spatio-Temporal Graph for Anomaly Detection in Surveillance Videos. In 2025 International Conference on Communication, Computer, and Information Technology (IC3IT) (pp. 1-7). IEEE.
[44] Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. NEJM, 380, 1347–1358.
[45] Kolla, S. K. (2021). Architectural Frameworks for Large-Scale Electronic Health Record Data Platforms. Current Research in Public Health, 1(1), 1–19. Retrieved from https://www.scipublications.com/journal/index.php/crph/article/view/1372
[46] Nagabhyru, K. C. (2025). Beyond Automation: The 2025 Role of Agentic AI in Autonomous Data Engineering and Adaptive Enterprise Systems.
[47] Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
[48] Lebcir, I., Mageswari, S. U., Bhosale, Y. H., Nagubandi, A. R., & Mahabooba, M. M. Agile Strategic Management in the Age of Disruption: Leveraging AI and Data Analytics for Competitive Advantage.
[49] Satyanarayanan, M. (2017). The emergence of edge computing. Computer, 50(1), 30–39.
[50] Velangani Divya Vardhan Kumar Bandi. (2024). Intelligent Data Platforms For Personalized Retail Analytics At Scale. Metallurgical and Materials Engineering, 30(4), 1011–1027. Retrieved from https://metall-mater-eng.com/index.php/home/article/view/1011-1027
[51] Sheller, M. J., Reina, G. A., Edwards, B., et al. (2020). Multi-institutional deep learning without sharing patient data. Brainlesion Workshop.
[52] Garapati, R. S., & Daram, D. S. B. (2025). AI-Enabled Predictive Maintenance Framework For Connected Vehicles Using Cloud-Based Web Interfaces. Available at SSRN 5524261.
[53] Shortliffe, E. H., & Sepúlveda, M. J. (2018). Clinical decision support in the era of AI. JAMA, 320(21), 2199–2200.
[54] Rongali, S. K. (2025, August). Deep Learning for Cybersecurity in Healthcare: A Mulesoft-Enabled Approach. In 2025 International Conference on Artificial Intelligence and Machine Vision (AIMV) (pp. 1-6). IEEE.
[55] Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning (2nd ed.). MIT Press.
[56] Siva Hemanth Kolla. (2023). Deep Learning–Driven Retrieval-Augmented Generation for Enterprise ITSM Automation: A Governance-Aligned Large Language Model Architecture . Journal of Computational Analysis and Applications (JoCAAA), 31(4), 2489–2502. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/4774
[57] Tsamados, A., Aggarwal, N., Cowls, J., et al. (2022). The ethics of algorithms. AI & Society, 37, 215–230.
[58] Davuluri, P. S. L. N. . (2024). AI-Driven Data Governance Frameworks for Automated Regulatory Reporting and Audit Readiness. Metallurgical and Materials Engineering, 30(4), 996–1010. Retrieved from https://metall-mater-eng.com/index.php/home/article/view/1936
[59] Wooldridge, M. (2009). An introduction to multiagent systems (2nd ed.). Wiley.
[60] Garapati, R. S. (2025). An Intelligent IoT Security System: Cloud-Native Architecture with Real-Time AI Threat Detection and Web Visualization. Journal homepage: https://jmsronline. com, 2(06).
[61] Zhang, A., Xing, L., Zou, J., & Wu, J. C. (2022). Shifting ML for healthcare to deployment. Nature Biomedical Engineering, 6, 1330–1345.
[62] GUNTUPALLI, R. (2025). EXPLAINABLE AI IN CLINICAL DECISION SUPPORT: INTERPRETABLE NEURAL MODELS FOR TRUSTWORTHY HEALTHCARE AUTOMATIONEXPLAINABLE AI IN CLINICAL DECISION SUPPORT: INTERPRETABLE NEURAL MODELS FOR TRUSTWORTHY HEALTHCARE AUTOMATION. TPM–Testing, Psychometrics, Methodology in Applied Psychology, 32(S9 (2025): Posted 15 December), 462-471.
[63] Benford, S., et al. (2009). Emergent multi-agent architectures. Autonomous Agents and Multi-Agent Systems, 18, 15–45.
[64] Inala, R. (2025). A Unified Framework for Agentic AI and Data Products: Enhancing Cloud, Big Data, and Machine Learning in Supply Chain, Insurance, Retail, and Manufacturing. EKSPLORIUM-BULETIN PUSAT TEKNOLOGI BAHAN GALIAN NUKLIR, 46(1), 1614-1628.
[65] Ferber, J. (1999). Multi-agent systems: An introduction. Addison-Wesley.
[66] Bandi, V. D. V. K. (2023). Production-Grade Machine Learning Pipelines For Healthcare Predictive Analytics. South Eastern European Journal of Public Health, 189–205. Retrieved from https://www.seejph.com/index.php/seejph/article/view/7057
[67] Kephart, J. O., & Chess, D. M. (2003). The vision of autonomic computing. Computer, 36(1), 41–50.
[68] Aitha, A. R., & Jyothi Babu, D. A. (2025). Agentic AI-Powered Claims Intelligence: A Deep Learning Framework for Automating Workers Compensation Claim Processing Using Generative AI. Available at SSRN 5505223.
[69] Huhns, M. N., & Singh, M. P. (1998). Readings in agents. Morgan Kaufmann.
[70] Amistapuram, K. (2025). GENERATIVE AI FOR CLAIMS EXCEPTIONS AND INVESTIGATIONS: ENHANCING RESOLUTION EFFICIENCY IN COMPLEX INSURANCE PROCESSES. Available at SSRN 5785482.
[71] Erl, T. (2016). Microservices design patterns. Prentice Hall.
[72] Gottimukkala, V. R. R. (2025). Generative AI for Exceptions and Investigations: Streamlining Resolution Across Global Payment Systems. Journal of International Commercial Law and Technology, 6(1), 969-972.
[73] Fowler, M. (2018). Refactoring (2nd ed.). Addison-Wesley.
[74] Segireddy, A. R. (2025). GENERATIVE AI FOR SECURE RELEASE ENGINEERING IN GLOBAL PAYMENT NETWORK. Lex Localis: Journal of Local Self-Government, 23.
[75] Gamma, E., Helm, R., Johnson, R., & Vlissides, J. (1994). Design patterns. Addison-Wesley.
[76] Amistapuram, K. (2025). Agentic AI for Next-Generation Insurance Platforms: Autonomous Decision-Making in Claims and Policy Servicing. Journal of Marketing & Social Research, 2, 88-103.
[77] Rieke, N., Hancox, J., Li, W., et al. (2020). Federated learning for digital health. NPJ Digital Medicine, 3, 119.
[78] Zaharia, M., et al. (2010). Spark: Cluster computing with working sets. HotCloud.
[79] Rongali, S. K., & Varri, D. B. S. (2025). AI in health care threat detection. World Journal of Advanced Research and Reviews, 25(3), 1784-1789.
[80] Lakshman, A., & Malik, P. (2010). Cassandra. ACM SIGOPS Operating Systems Review, 44(2), 35–40.
[81] Nagubandi, A. R. (2025). PIONEERING SELF-ADAPTIVE AI ORCHESTRATION ENGINES FOR REAL-TIME END-TO-END MULTI-COUNTERPARTY DERIVATIVES, COLLATERAL, AND ACCOUNTING AUTOMATION: INTELLIGENCE-DRIVEN WORKFLOW COORDINATION AT ENTERPRISE SCALE. Lex Localis, 23(S6), 8598-8610.
[82] Stonebraker, M., & Çetintemel, U. (2005). One size fits all? ICDE Proceedings, 2–11.
[83] Yandamuri, U. S. (2022). Big Data Pipelines for Cross-Domain Decision Support: A Cloud-Centric Approach. International Journal of Scientific Research and Modern Technology, 227.
[84] Moreira, M. W. L., et al. (2018). IoT-based smart healthcare systems. Sensors, 18(4), 1155.
[85] Guntupalli, R. (2025). Multi-Cloud vs. Hybrid Cloud Security: Key Challenges and Best Practices. Hybrid Cloud Security: Key Challenges and Best Practices (November 21, 2025).
[86] Mell, P., & Grance, T. (2011). The NIST definition of cloud computing. NIST.
[87] Pamisetty, A., Paleti, S., Adusupalli, B., Singireddy, J., Inala, R., & Nagabhyru, K. C. (2025, September). Explainable AI Systems for Credit Scoring and Loan Risk Assessment in Digital Banking Platforms. In 2025 IEEE 13th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) (pp. 1478-1483). IEEE.
[88] World Health Organization. (2021). Ethics and governance of artificial intelligence for health. WHO Press.
[89] [Kolla, S. H. (2024). RETRIEVAL-AUGMENTED GENERATION WITH SMALL LLMS FOR KNOWLEDGE-DRIVEN DECISION AUTOMATION IN ENTERPRISE SERVICE PLATFORMS. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(3), 476–486. https://doi.org/10.61841/turcomat.v15i3.15497
[90] Moreau, L., et al. (2015). The W3C PROV family of specifications. Future Generation Computer Systems, 29(7), 161–165.
[91] Rongali, S. K. (2025, August). AI-Powered Threat Detection in Healthcare Data. In 2025 International Conference on Artificial Intelligence and Machine Vision (AIMV) (pp. 1-7). IEEE.
[92] Jennings, N. R., & Wooldridge, M. (1998). Applications of intelligent agents. Springer.
[93] Van Roy, P. (2009). Self-management in distributed systems. IEEE Computer, 42(12), 40–47.
[94] Vardhan Kumar Bandi, V. D. (2024). Automated Feature Engineering Systems in Large-Scale Healthcare Data Environments. Journal of Neonatal Surgery, 13(1), 2127–2141. Retrieved from https://www.jneonatalsurg.com/index.php/jns/article/view/10004
[95] Kairouz, P., McMahan, H. B., Avent, B., et al. (2021). Advances and open problems in federated learning. Foundations and Trends in Machine Learning, 14(1–2), 1–210.
[96] Nagabhyru, K. C., & Babu, A. J. Human In The Loop Generative AI: Redefining Collaborative Data Engineering For High Stakes Industries.
[97] McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. y. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 1273–1282.
