Transformative AI Framework for Data Mining and Federated Learning in Financial Intelligence and Smart Healthcare
DOI:
https://doi.org/10.15680/IJCTECE.2023.0606025Keywords:
Federated Learning, Distributed Systems, Financial Services, Healthcare, Data Privacy, Secure AI, Blockchain, Differential PrivacyAbstract
The rapid growth of data-intensive applications in financial services and healthcare has necessitated advanced computational frameworks that ensure data security, privacy, and efficiency. Traditional centralized machine learning approaches often require aggregating sensitive data into a single location, posing significant privacy and regulatory challenges. Federated Learning (FL) emerges as a paradigm that allows multiple decentralized entities to collaboratively train machine learning models while keeping raw data localized. This study proposes an advanced AI and federated learning framework tailored for secure distributed systems in financial services and healthcare. The framework leverages state-of-the-art AI techniques, including deep learning, reinforcement learning, and privacy-preserving mechanisms like differential privacy and secure multi-party computation. By integrating FL with blockchain-based auditability and encryption protocols, the system ensures robust data confidentiality and integrity. The proposed framework is evaluated across real-world healthcare and financial datasets, demonstrating significant improvements in model accuracy, reduced communication overhead, and compliance with privacy regulations such as GDPR and HIPAA. The results indicate that adopting this federated approach can enable institutions to harness distributed intelligence while mitigating risks associated with data breaches and regulatory non-compliance, paving the way for next-generation secure, collaborative AI in sensitive domains.
References
1. Thota, S. (2023). Federated Learning Approaches for Privacy-Preserving Artificial Intelligence in Distributed Cloud Environments. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(3), 118-127.
2. Kumar, P., Gupta, A., & Singh, R. (2022). Artificial intelligence applications in cloud computing security A review. Journal of Network and Computer Applications, 198, 103241. Elsevier.
3. Lytras, M. D., Sarirete, A., Damiani, E., & Visvizi, A. (2021). Artificial intelligence and big data analytics for smart healthcare. Elsevier.
4. Ravi Kumar Ireddy, " AI Driven Predictive Vulnerability Intelligence for Cloud-Native Ecosystems" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 2, pp.894-903, March-April-2023. Available at doi : https://doi.org/10.32628/CSEIT2342438
5. Sanepalli, Uttama Reddy. (2023). Cognitive goal-driven financial infrastructure: A cloud-native, AI-orchestrated architecture for investment trade settlement and risk management systems. World Journal of Advanced Research and Reviews, 19(1), 1659–1667. https://doi.org/10.30574/wjarr.2023.19.1.1358
6. Mudunuri, P. R. (2022). Engineering audit-ready CI/CD pipelines for federally regulated scientific computing. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(5), 5342-5351.
7. Dama, H. B. (2023). Designing Highly Available Multi-Cloud Database Architectures for Global Financial Services. International Journal of Research and Applied Innovations, 6(1), 8329-8336.
8. Paul, D., Namperumal, G., & Surampudi, Y. (2023). Optimizing llm training for financial services: best practices for model accuracy, risk management, and compliance in ai-powered financial applications. Journal of Artificial Intelligence Research and Applications, 3(2), 550-588.
9. Mathur, T., Muthusamy, P., & Mohammed, A. S. (2019). Federated Learning for Performance Anomaly Detection in Distributed Data Centers. European Journal of Quantum Computing and Intelligent Agents, 3, 33-66.
10. Panda, S. S. (2023). Smart Machines, Smarter Outcomes the Rise of Self-Learning Systems. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(5), 9004-9015.
11. Jagadeesh, S., & Sugumar, R. (2017). Optimal knowledge extraction system based on GSA and AANN. International Journal of Control Theory and Applications, 10(12), 153–162.
12. Meka, S. (2023). Building Digital Banking Foundations: Delivering End-to-End FinTech Solutions with Enterprise-Grade Reliability. International Journal of Research and Applied Innovations, 6(2), 8582-8592.
13. Mohana, P., Muthuvinayagam, M., Umasankar, P., & Muthumanickam, T. (2022, March). Automation using Artificial intelligence based Natural Language processing. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1735-1739). IEEE.
14. Ramsugeerthi, A., Neela Madheswari, A., Umamaheswari, A., & Prassana, D. (2020). Location navigation assistance for educational institutions using augmented reality. Journal of Xidian University, 14(4), 1342–1347. https://doi.org/10.37896/jxu14.4/156
15. Rengarajan, A., & Rajagopalan, S. (2021). Chaos Blend LFSR-Duo Approach on FPGA for Medical Image Security. Emerging Technologies in Data Mining and Information Security: Proceedings of IEMIS 2020, Volume 3, 3, 155.
16. Prasad, P. K. (2021). Kubernetes everywhere: Operating hybrid and multi-cloud infrastructure at scale. International Journal of Engineering & Extended Technologies Research, 3(4), 3393–3401.
17. Adepu, R. (2022). Building secure multi-cloud infrastructure for mission-critical enterprise workloads. The International Journal of Research Publications in Engineering, Technology and Management, 5(5), 14–32.
18. Mallireddy, S. (2022). Digital services and usage of ServiceNow among patients and citizens living at homes. International Journal of Future Innovative Science and Technology, 5(2), 1–3.
19. Panyala, V. R., & Pappu, H. (2021). Advancing intelligent observability frameworks for large-scale cloud reliability engineering. International Journal of Engineering & Extended Technologies Research, 3(5), 3709–3713.
20. Sharma, Ankit and Mulgund, Pavankumar and Srivastava, Adarsh and Agrawal, Lavlin, Beyond Cryptocurrency: There’s More to Blockchain (January 07, 2020). Beyond Cryptocurrency: There's More to Blockchain," Amplify, Cutter Consortium, January 7, 2020., Available at SSRN: https://ssrn.com/abstract=6098906 or http://dx.doi.org/10.2139/ssrn.6098906
21. Subramanyam, S. P. (2022). Kubernetes-oriented continuous deployment architecture for .NET microservices. International Journal of Future Innovative Science and Technology (IJFIST), 5(3), 8482–8490. https://doi.org/10.15662/IJFIST.2022.0503002
22. Adepu, G. (2022). Machine learning-driven environmental monitoring systems for real-time regulatory compliance and risk detection. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(2), 22–37.
23. Namdeo, A. (2022). Federated learning BI across multi-cloud data silos. The International Journal of Research Publications in Engineering, Technology and Management, 5(6), 7893–7903.
24. Siddiqui, M. I. H., Rahman, M. S., Kabir, A. A., Mahmud, F. U., Rashid, S. U., & Shammah, R. S. (2023). Comparative analysis of explainable machine learning models for cancer classification using cytological features. Journal of Medical and Health Studies, 4(5), 110-150
25. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
26. Vijayakumar, R., & Madheswaran, M. (2017, March). Modal analysis of femur bone using finite element method for healthcare system. In 2017 Conference on Emerging Devices and Smart Systems (ICEDSS) (pp. 224-228). IEEE.
27. S. Roy and S. Saravana Kumar, “Feature Construction Through Inductive Transfer Learning in Computer Vision,” in Cybernetics, Cognition and Machine Learning Applications: Proceedings of ICCCMLA 2020, Springer, 2021, pp. 95–107.
28. Archana, R., & Anand, L. (2023, May). Effective Methods to Detect Liver Cancer Using CNN and Deep Learning Algorithms. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (pp. 1-7). IEEE.
29. Hebbar, K. S. (2022). Machine learning-assisted service boundary detection for modularizing legacy systems. International Journal of Applied Engineering & Technology, 4(2), 401–414.
30. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
31. Garg, V. K., Soundappan, S. J., & Kaur, E. M. (2020). Enhancement in intrusion detection system for WLAN using genetic algorithms. South Asian Research Journal of Engineering and Technology, 2(6), 62–64. https://doi.org/10.36346/sarjet.2020.v02i06.003
32. Hussain, S., Nanda, S. K., Barigidad, S., Akhtar, S., Suaib, M., & Ray, N. K. (2021, December). Novel deep learning architecture for predicting heart disease using CNN. In 2021 19th OITS international conference on information technology (OCIT) (pp. 353-357). IEEE.
33. Revathi, K. G., Ananth, B. J., Saravanan, M. L., & Kumar, A. R. (2021). Gps enabled vehicle location identification using gsm and fare collection using smart card. Turkish journal of computer and mathematics education, 12(10), 2657-2668.
34. Niture, N. A., & Abdellatif, I. (2020, October). Ai based airplane air pollution identification architecture using satellite imagery. In 2020 IEEE Cloud Summit (pp. 150-155). IEEE.
35. Kothokatta, L. (2020). Scalable validation and continuous verification of AI/ML systems on AWS using Python-based automation. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 3(5), 5131–5138.
36. Lakshmi, A. J., Dasari, R., Chilukuri, M., Tirumani, Y., Praveena, H. D., & Kumar, A. P. (2023, May). Design and Implementation of a Smart Electric Fence Built on Solar with an Automatic Irrigation System. In 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC) (pp. 1553-1558). IEEE.
37. Al-Issa, Y., Ottom, M. A., & Tamrawi, A. (2021). eHealth cloud security challenges and solutions A survey. Journal of Network and Computer Applications, 178, 102973. Elsevier.
38. Kumar, P., Gupta, A., & Singh, R. (2022). Artificial intelligence applications in cloud computing security A review. Journal of Network and Computer Applications, 198, 103241. Elsevier
