Federated Learning for Cross-Institutional Fraud Monitoring in National Healthcare Security
Keywords:
Federated Learning, Healthcare Fraud Detection, National Healthcare Security, Privacy-Preserving Machine Learning, Cross-Institutional Collaboration, Secure Aggregation, Differential Privacy, Anomaly Detection, Distributed Machine Learning, Data Security, Healthcare Analytics, Regulatory ComplianceAbstract
Healthcare fraud poses a significant threat to national healthcare security, leading to substantial financial losses, reduced service quality, and compromised patient trust. Traditional centralized fraud detection systems require aggregating sensitive patient and institutional data into a single repository, raising serious concerns regarding privacy, data ownership, and regulatory compliance. To address these challenges, this paper proposes a Federated Learning (FL)-based framework for cross-institutional fraud monitoring in national healthcare systems. The proposed approach enables multiple healthcare institutions, including hospitals, insurance providers, and regulatory bodies-to collaboratively train machine learning models without sharing raw data. Instead, locally trained model updates are securely aggregated to produce a global fraud detection model, preserving data privacy while leveraging diverse and distributed datasets.
The framework integrates secure aggregation protocols, differential privacy mechanisms, and anomaly detection algorithms tailored to healthcare fraud patterns such as billing irregularities, duplicate claims, and abnormal treatment procedures. Experimental evaluations using simulated multi-institutional healthcare datasets demonstrate that the federated model achieves detection performance comparable to centralized approaches while significantly reducing privacy risks and regulatory barriers. Furthermore, the system enhances robustness against data heterogeneity and institutional bias through adaptive model weighting and continuous learning mechanisms.
This research highlights the potential of federated learning as a scalable, privacy-preserving solution for national-level healthcare fraud monitoring. By enabling secure cross-institutional collaboration, the proposed framework strengthens healthcare security infrastructure, promotes trust among stakeholders, and supports data-driven policy enforcement without compromising sensitive patient information
References
1. Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mirov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differential privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 308–318. https://doi.org/10.1145/2976749.2978318
2. Acar, A., Aksu, H., Uluagac, A. S., & Conti, M. (2018). A survey on homomorphic encryption schemes: Theory and implementation. ACM Computing Surveys (CSUR), 51(4), 1–35.
3. Alam, M. A., Sohel, A., Hossain, A., Eshra, S. A., & Mahmud, S. (2023). Medical Imaging For Early Cancer Diagnosis And Epidemiology Using Artificial Intelligence: Strengthing National Healthcare Frameworks InThe Usa. American Journal of Scholarly Research and Innovation, 2(01), 24-49.
4. Aronno, M. S. R., Zumma, M. T., Prodhan, R., Zohora, F. T., Sakib, N., &Tahmiduzzaman, K. B. M. (2023, July). A study of cyber bullying classification using Social Media and Texual analysis based on Machine Learning Approches. In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-8). IEEE.
5. Bauder, R. A., Khoshgoftaar, T. M., &Seliya, N. (2017). A survey on the state of healthcare upcoding fraud analysis and detection. Health Information Science and Systems, 5(1), 3. https://doi.org/10.1007/s13755-017-0024-z
6. Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2017). Practical secure aggregation for privacy-preserving machine learning. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 1175–1191.
7. Chawla, N. V., Bowyer, K. W., Hall, L. O., &Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357.
8. Cohen, I. G., & Mello, M. M. (2018). HIPAA and protecting health information in the 21st century. JAMA, 320(3), 249–250. https://doi.org/10.1001/jama.2018.5630
9. Dou, Q., So, T. Y., Jiang, M., Liu, Q., Vardhanabhuti, V., Pang, G., & Heng, P. A. (2021). Federated deep learning for detecting COVID-19 lung abnormalities in CT images: A privacy-preserving multicenter study. NPJ Digital Medicine, 4(1), 1–11.
10. Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science, 9(3–4), 211–407.
11. Eshra, S. A., Zohora, F. T., Akter, S., Rasul, I., & Hossain, A. (2025). The role of threat intelligence in preventing financially motivated cyberattacks. Journal of Engineering and Computational Intelligence Review, 3(2), 20-37.
12. Federal Bureau of Investigation (FBI). (2023). Health care fraud. FBI News & Features. https://www.fbi.gov/investigate/white-collar-crime/health-care-fraud
13. Fredrikson, M., Jha, S., &Ristenpart, T. (2015). Model inversion attacks that exploit confidence information and basic countermeasures. Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, 1322–1333.
14. He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263–1284.
15. Hsieh, K., Phanishayee, A., Mutlu, O., & Gibbons, P. (2020). The non-IID data quagmire of decentralized machine learning. Proceedings of the 37th International Conference on Machine Learning (ICML), 4387–4398.
16. Islam, M. S., & Shiva, T. A. (2024). Virtual cognitive behavioural therapy in rural US communities: Effectiveness and reach. Journal of Business Insight and Innovation, 3(2), 60-76.
17. Joudaki, H., Rashidian, A., Minaei-Bidgoli, B., Mahmoodi, M., Geraili, B., Nasiri, M., & Arab, M. (2015). Improving fraud and abuse detection in general physician claims: A data mining study. International Journal of Health Policy and Management, 4(3), 165.
18. 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.
19. Liu, J., Huang, J., Qin, Y., & Li, C. (2022). Privacy-preserving anomaly detection in medical insurance claims via federated learning. Journal of Healthcare Engineering, 2022, Article ID 8492011.
20. McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 1273–1282.
21. Mironov, I. (2017). Rényi differential privacy. 2017 IEEE 30th Computer Security Foundations Symposium (CSF), 263–275.
22. Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., ... & Cardoso, M. J. (2020). The future of digital health with federated learning. NPJ Digital Medicine, 3(1), 1–7. https://doi.org/10.1038/s41746-020-00323-1
23. Rasul, I., Akter, T., Akter, S., Eshra, S. A., & Hossain, A. (2025). AI-Driven Business Analytics for Product Development: A Survey of Techniques and Outcomes in the Tech Industry. Frontline Marketing. Management and Economics Journal, 5(01), 16-38.
24. Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., ... & Bakas, S. (2020). Federated learning in medicine: Facilitating multi-institutional collaborations without sharing patient data. Scientific Reports, 10(1), 1–12.
25. Shiva, T. A., Brown, J. G., McField, A. A., Osborne, R. E., Oberle, C. D., Shiva, T. A., ... & Oberle, C. D. (2025). Asian American Journal of Psychology.
26. Thornton, D., Mueller, R. M., Schoutsen, P., & van Hillegersberg, J. (2013). Predicting healthcare fraud in Medicaid: A case study using data mining. Decision Support Systems, 55(1), 124–134.
27. The Role of Threat Intelligence in Preventing Financially Motivated Cyberattacks. (2025). Journal of Engineering and Computational Intelligence Review, 3(2), 20-37. https://jecir.com/index.php/jecir/article/view/24
28. Touhida Akter, Nusrat Ireen, Tasnia Akter Shiva, Sheikh Habiba Amjad. (2026). Immersive Intelligence: Using Adaptive Virtual Reality and Artificial Intelligence to Enhance Social Cognition and Workforce Readiness for Young Adults with Autism Spectrum Disorder. International Journal of Research & Technology, 14(1), 240–264. https://doi.org/10.64882/ijrt.v14.i1.916
29. Zohora, F. T., Parveen, R., Nishan, A., Haque, M. R., & Rahman, S. (2024). Optimizing credit card security using consumer behavior data: A big data and machine learning approach to fraud detection. Frontline Mark. Manag. Econ. J, 4(12), 26-60.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
