Federated AI Architectures for Secure Multi-Organization Healthcare Data Analysis
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806031Keywords:
Federated Learning, Privacy-Preserving Machine Learning, Secure Multi-Party Computation (SMPC), Healthcare Data Collaboration, Distributed AI Systems, Differential Privacy, Data Governance in Healthcare, Secure Model Aggregation, Cross-Institutional Data Analysis, Homomorphic Encryption, Decentralized Machine Learning, Clinical Data Sharing Frameworks, Privacy-Aware AI Architectures, Interoperable Healthcare Systems, Trustworthy AI in HealthcareAbstract
The difficulties of working in sectors with strict privacy constraints, such as healthcare, result from the fact that data is separated and distributed across multiple sources that are unable to exchange it. The solution to this dilemma is to allow multiple data owners to collaborate on a joint task by using shared/global models while keeping the data decentralized. Three paradigms of federated machine learning are proposed: horizontal federated learning, vertical federated learning, and federated transfer learning. Federated learning processes and architectures with privacy-preserving properties that also address data provenance, access control, and interoperability.
Integrating heterogeneous distributed data sources while maintaining privacy is a major challenge for modern data and AI analysis. Sensitive data in particular, such as healthcare data, privacy is guaranteed by law. Therefore, many healthcare data sources are available based on these data sharing and data monetization are a difficult integration task. Multiple organizations are willing to collaborate by sharing insights from their private datasets, but without sharing the original datasets because healthcare data is highly sensitive. However, existing guidelines permit the use of healthcare data in Europe and America only for either research or health benefits, and not even for commercial use. Nevertheless, several companies are attempting to break this barrier by using fake-generated data for AI modeling purposes. As a result, three main criteria must be considered during the establishment of collaboration for multi-organization AI data modeling: privacy, security, and monitoring.
References
1. 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).
2. Mell, P., & Grance, T. (2011). The NIST definition of cloud computing. NIST.
3. Sheelam, G. K. (2025). Agentic AI in 6G: Revolutionizing Intelligent Wireless Systems through Advanced Semiconductor Technologies. Advances in Consumer Research.
4. World Health Organization. (2021). Ethics and governance of artificial intelligence for health. WHO Press.
5. 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
6. Moreau, L., et al. (2015). The W3C PROV family of specifications. Future Generation Computer Systems, 29(7), 161–165.
7. Davuluri, P. N. Integrating Artificial Intelligence into Event-Driven Financial Crime Compliance Platforms.
8. Van Roy, P. (2009). Self-management in distributed systems. IEEE Computer, 42(12), 40–47.
9. 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
10. Sutton, R. S. (2019). The bitter lesson. Incomplete Ideas Blog.
11. Floridi, L. (2013). The ethics of information. Oxford University Press.
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. Dehghani, Z. (2022). Data mesh. O’Reilly Media.
16. Sriram, H. K., Gadi, A. L., & Challa, K. (2025). Leveraging AI, ML, and Gen AI in Automotive and Financial Services: Data-Driven Approaches to Insurance, Payments, Identity Protection, and Sustainable Innovation. Anil Lokesh and Challa, Kishore and singreddy, Sneha, Leveraging AI, ML, and Gen AI in Automotive and Financial Services: Data-Driven Approaches to Insurance, Payments, Identity Protection, and Sustainable Innovation (March 25, 2025).
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. He, J., Baxter, S., Xu, J., et al. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25, 30–36.
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. 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
31. Jennings, N. R., & Wooldridge, M. (1998). Applications of intelligent agents. Springer.
32. 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
33. Kelly, C. J., Karthikesalingam, A., Suleyman, M., et al. (2019). Key challenges for delivering clinical impact with AI. BMC Medicine, 17, 195.
34. 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.
35. Koller, D., & Friedman, N. (2009). Probabilistic graphical models. MIT Press.
36. 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.
37. Liu, F., et al. (2025). Foundational architecture for AI agents in healthcare. Cell Reports Medicine, 6(10), 102374.
38. 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.
39. Moreau, L., & Groth, P. (2013). Provenance: An introduction to PROV. Morgan & Claypool.
40. 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.
41. Obermeyer, Z., & Emanuel, E. (2016). Predicting the future—Big data and clinical medicine. NEJM, 375, 1216–1219.
42. 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
43. Pearl, J. (2009). Causality (2nd ed.). Cambridge University Press.
44. 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.
45. Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. NEJM, 380, 1347–1358.
46. 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.
47. Rieke, N., Hancox, J., Li, W., et al. (2020). Federated learning for digital health. NPJ Digital Medicine, 3, 119.
48. Challa, K., Sriram, H. K., & Gadi, A. L. (2025). Leveraging AI, ML, and Gen AI in Automotive and Financial Services: Data-Driven Approaches to Insurance, Payments, Identity Protection, and Sustainable Innovation.
49. Amershi, S., Begel, A., Bird, C., et al. (2019). Software engineering for machine learning: A case study. Proceedings of the International Conference on Software Engineering, 291–300.
50. 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.
51. 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.
52. 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).
53. Batini, C., & Scannapieco, M. (2016). Data and information quality: Dimensions, principles and techniques. Springer.
54. 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.
55. Benjamens, S., Dhunnoo, P., & Meskó, B. (2020). The state of artificial intelligence-based FDA-approved medical devices. NPJ Digital Medicine, 3, 118.
56. Nagabhyru, K. C. (2025). Beyond Automation: The 2025 Role of Agentic AI in Autonomous Data Engineering and Adaptive Enterprise Systems.
57. Bertsekas, D. P. (2012). Dynamic programming and optimal control (Vol. 1). Athena Scientific.
58. 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.
59. Brundage, M., Avin, S., Clark, J., et al. (2018). The malicious use of artificial intelligence. arXiv.
60. 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.
61. Ferber, J. (1999). Multi-agent systems: An introduction. Addison-Wesley.
62. 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.
63. Kephart, J. O., & Chess, D. M. (2003). The vision of autonomic computing. Computer, 36(1), 41–50.
64. 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.
65. Huhns, M. N., & Singh, M. P. (1998). Readings in agents. Morgan Kaufmann.
66. Nagabhyru, K. C., & Babu, A. J. Human In The Loop Generative AI: Redefining Collaborative Data Engineering For High Stakes Industries.
67. Erl, T. (2016). Microservices design patterns. Prentice Hall.
68. 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.
69. Fowler, M. (2018). Refactoring (2nd ed.). Addison-Wesley.
70. Segireddy, A. R. (2025). GENERATIVE AI FOR SECURE RELEASE ENGINEERING IN GLOBAL PAYMENT NETWORK. Lex Localis: Journal of Local Self-Government, 23.
71. Gamma, E., Helm, R., Johnson, R., & Vlissides, J. (1994). Design patterns. Addison-Wesley.
72. Amistapuram, K. (2025). GENERATIVE AI FOR CLAIMS EXCEPTIONS AND INVESTIGATIONS: ENHANCING RESOLUTION EFFICIENCY IN COMPLEX INSURANCE PROCESSES. Available at SSRN 5785482.
73. Zaharia, M., et al. (2010). Spark: Cluster computing with working sets. HotCloud.
74. Singireddy, S. (2025, May). AI-Driven Comprehensive Insurance and AAA Membership Benefits Overview. In 2025 2nd International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE) (pp. 1-13). IEEE.
75. Lakshman, A., & Malik, P. (2010). Cassandra. ACM SIGOPS Operating Systems Review, 44(2), 35–40.
76. 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.
77. Stonebraker, M., & Çetintemel, U. (2005). One size fits all? ICDE Proceedings, 2–11.
78. 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.
79. Moreira, M. W. L., et al. (2018). IoT-based smart healthcare systems. Sensors, 18(4), 1155.
80. Challa, K. (2025). Innovations in Digital Finance and Intelligent Technologies: A Deep Dive into AI, Machine Learning, Cloud Computing, and Big Data in Transforming Global Payments and Financial Services. Deep Science Publishing.
81. 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.
82. Kumar, S. S., Singireddy, S., Nanan, B. P., Recharla, M., Gadi, A. L., & Paleti, S. (2025). Optimizing edge computing for big data processing in smart cities. Metallurgical and Materials Engineering, 31(3), 31-39.
83. 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.
84. Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 160.
85. 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.
86. Shortliffe, E. H., & Sepúlveda, M. J. (2018). Clinical decision support in the era of AI. JAMA, 320(21), 2199–2200.
87. Annapareddy, V. N., Challa, K., Komaragiri, V. B., Sriram, H. K., Kalisetty, S., & Goma, T. (2025, April). Explaining AI Techniques such as SHAP LIME and RISE in Limited Sample Size Neuroimaging Studies. In 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0 (pp. 1-6). IEEE.
88. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning (2nd ed.). MIT Press.
89. 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
90. Tsamados, A., Aggarwal, N., Cowls, J., et al. (2022). The ethics of algorithms. AI & Society, 37, 215–230.
91. 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
92. Wooldridge, M. (2009). An introduction to multiagent systems (2nd ed.). Wiley.
93. 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
94. Zhang, A., Xing, L., Zou, J., & Wu, J. C. (2022). Shifting ML for healthcare to deployment. Nature Biomedical Engineering, 6, 1330–1345.
95. 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
96. McMahan, B., Moore, E., Ramage, D., Hampson, S., & Aguera y Arcas, B. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 1273–1282.
97. Annapareddy, V. N., Singireddy, J., Preethish Nanan, B., & Burugulla, J. K. R. (2025). Emotional Intelligence in Artificial Agents: Leveraging Deep Multimodal Big Data for Contextual Social Interaction and Adaptive Behavioral Modelling. Jai Kiran Reddy, Emotional Intelligence in Artificial Agents: Leveraging Deep Multimodal Big Data for Contextual Social Interaction and Adaptive Behavioral Modelling (April 14, 2025).
98. Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., & Bakas, S. (2020). Federated learning in medicine: Facilitating multi-institutional collaborations without sharing patient data. Scientific Reports, 10(1), 12598.
99. Nandan, B. P. (2022). AI-Powered Fault Detection In Semiconductor Fabrication: A Data-Centric Perspective.
100. Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H., Albarqouni, S., Bakas, S., Galtier, M., Landman, B., Maier-Hein, K., Ourselin, S., Sheller, M., Summers, R., Trask, A., Xu, D., Baust, M., & Cardoso, M. (2020). The future of digital health with federated learning. npj Digital Medicine, 3(1), 119.
101. Sheelam, G. K., & Nandan, B. P. (2022). Integrating AI And Data Engineering For Intelligent Semiconductor Chip Design And Optimization. Migration Letters, 19, 2178-2207.
102. 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.
