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Original Article

Hybrid Split-Federated Learning Framework for AI Driven Protection of Student Fee Payment Systems in Higher Education Institutions

Dr. M. Anita Indu1
1 Assistant Professor, PG Department of Computer Science, Shri Shankarlal Sundarbai Shasun Jain College for Women, Chennai, Tamilnadu, India.

Published Online: January-April 2026

Pages: 492-495

References

1. Allerin(2024), “Four Key Differences between Federated Learning and Classical ML”. Available
at: 〈https://www.allerin.com/blog/4-key-differences-between- federated-learning-and-classical-machine-learning〉.
2. Convergence.io(2023). “ Federated Learning vs Centralized Machine Learning: A Convergence Comparison “. Available at: 〈
https://www.convergenc e.io/blog/federated-learning-vs-centralized-machine-learning〉.
3. Chandra, R. (2025). “Security and Privacy Testing Automation for LLM-Enhanced Applications in Mobile Devices”. 5(02), 30–41.
https://doi.org/10.55640/ijns-05-02-02
4. J. Xing, J. Tian, Z. Jiang, J. Cheng, H. Yin, “Jupiter: a modern federated learning platform for regional medical care”, Sci. China Inf. Sci. 64
(2021) 1–14.
5. Readthedocs.io.” What is Federated Learning? — NVIDIA FLARE 2.4.0 documentation”. [online] Available at:〈
https://nvflarereadthedocs.io/en/2.4/fl_ introduction.html〉2024.
6. Hukkeri, G. S., Goudar, R. H., Dhananjaya, G. M., Rathod, V. N., &Ankalaki, S. (n.d.). “Split-fed learning: A deep dive into methods,
innovations and future prospects for data privacy and efficiency in decentralized machine learning”. IEEE Access.
https://doi.org/10.1109/access.2025.3547641
7. Deng, R., Du, X., Lu, Z., Duan, Q., Huang, S.-C., & Wu, J. (2023). “HSFL: Efficient and Privacy-Preserving Offloading for Split and
Federated Learning in IoT Services”. 658–668. https://doi.org/10.1109/icws60048.2023.00084
8. Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Qiang Yang, Dusit Niyato, and Chunyan
Miao. Federated learning in mobile edge networks: A comprehensive survey. IEEE Communications Surveys Tutorials, 22 ( 3 ): 2031–2063,
2020.
9. Ang Li, Jingwei Sun, Pengcheng Li, Yu Pu, Hai Li, and Yiran Chen. “Hermes: an efficient federated learning framework for heterogeneous
mobile clients”. In Proceedings of the 27th Annual International Conference on Mobile Computing and Networking, pages 420–437, 2021.
10. Baduwal, M., Paudel, P., & Chaudhary, V. (2026). “Federated Learning: A Survey of Core Challenges, Current Methods, and Opportunities”.
11. Yin, B., Chen, Z., & Tao, M. (2023). “Predictive GAN-Powered Multi-Objective Optimization for Hybrid Federated Split Learning”. IEEE
Transactions on Communications, 71(8), 4544–4560. https://doi.org/10.1109/tcomm.2023.3277878
12. H. Davis, "Smart Cities and Federated Learning: A Case Study". IEEE Internet of Things Journal, vol. 7, no. 6, pp. 4567-4575, 2020.
13. I. Miller, "Communication Protocols for Split-Fed Learning," IEEE Transactions on Information Theory, vol. 67, no. 8, pp. 1234-1245,2021.
14. J. Wilson, "Performance Analysis of Split-Fed Learning,"International Conference on Machine Learning, pp. 789-795, 2022.
15. K. Anderson, "Predictive Modeling in Finance Using Split-Fed Learning," Journal of Financial Technology, vol. 5, no. 1, pp. 34-45, 2021.
16. L. Thomas, "Secure Recommendation Systems with Split-Fed Learning," IEEE Transactions on Knowledge and Data Engineering,
vol. 33, no. 7, pp. 1234-1245, 2021.
17. M. Jackson, "Addressing Non-IID Data in Federated Learning, "Journal of Machine Learning Research, vol. 22, no. 1, pp. 1- 15,2021.
18. N. White, "Enhancing Security in Split-Fed Learning," IEEE Security and Privacy, vol. 19, no. 5, pp. 56-64, 2021.
19. O. Harris, "Integrating Blockchain with Federated Learning," IEEE Transactions on Emerging Topics in Computing, vol. 10, no. 2, pp.234-
245, 2022.
20. Ji Liu, Jizhou Huang, Yang Zhou, Xuhong Li, Shilei Ji, Haoyi Xiong,D. Dou. From distributed machine learning to federated learning:
asurvey. Knowledge and Information Systems. Pp. 1-36. 2021.https://doi.org/10.1007/s10115-022-01664-x

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