Authors : Arya K. S.; Aparna A.

Volume/Issue : Volume 11 - 2026, Issue 3 - March

---

Google Scholar : https://tinyurl.com/y5nx6ky6

Scribd : https://tinyurl.com/4nfe225n

DOI : https://doi.org/10.38124/ijisrt/26mar171

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

---

Abstract : Federated Learning (FL) allows decentralized model training while maintaining data locality, yet remains vulnerable to gradient-based leakage. This paper examines core FL algorithms and integrates privacy-preserving techniques, specifically Differential Privacy, Homomorphic Encryption, and Secure Aggregation. We analyze the performance trade-offs between security and computational efficiency, establishing a framework for secure collaborative AI.

Keywords : Federated Learning; Privacy Preservation; Secure Aggregation; Homomorphic Encryption; Differential Privacy.

References :

1. T. Lia and M. Togan, "Secure Aggregation Protocols for Privacy-Preserving Federated Learning," International Journal of Computer Science and Security, vol. 14, no. 2, 2023.
2.  X. Fang and W. Qian, "PFMLP: A Privacy-Preserving Machine Learning Framework using Partially Homomorphic Encryption," Journal of Network and Computer Applications, 2024.
3. O. Akinsiku, "A Comprehensive Survey of Federated Learning Approaches for Privacy-Preserving Machine Learning," IEEE Access, 2023.
4. M. Hasan, "Federated Learning for Enterprise Decision Systems: A Systematic PRISMA Review," Expert Systems with Applications, 2025.
5. D. C. Nguyen, M. Ding, P. N. Pathirana, A. Seneviratne, J. Li, and H. V. Poor, “Federated learning for internet of things: A comprehensive survey,” IEEE Commun. Surv. Tutorials, vol. 23, no. 3, pp. 1622–1658, 2021.
6. H. U. Manzoor, A. Shabbir, A. Chen, D. Flynn, and A. Zoha, “A survey of security strategies in federated learning: Defending models, data, and privacy,” Futur. Internet, vol. 16, no. 10, p. 374, 2024.
7.    E. T. M. Beltrán et al., “Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges,” IEEE Commun. Surv. Tutorials, vol. 25, no. 4, pp. 2983–3013, 2023.
8. C. Chen et al., “Trustworthy federated learning: privacy, security, and beyond,” Knowl. Inf. Syst., vol. 67, no. 3, pp. 2321–2356, 2025.
9. Y. Liu et al., “Vertical federated learning: Concepts, advances, and challenges,” IEEE Trans. Knowl. Data Eng., vol. 36, no. 7, pp. 3615–3634, 2024.
10. M. K. Kundalwal, A. Saraswat, I. Mishra, and D. Mishra, “Client Contribution Normalization for Enhanced Federated Learning,” arXiv Prepr. arXiv2411.06352, 2024.
11. M. Moshawrab, M. Adda, A. Bouzouane, H. Ibrahim, and A. Raad, “Reviewing federated learning aggregation algorithms; strategies, contributions, limitations and future perspectives,” Electronics, vol. 12, no. 10, p. 2287, 2023.             
12. S. Banabilah, M. Aloqaily, E. Alsayed, N. Malik, and Y. Jararweh, “Federated learning review: Fundamentals, enabling technologies, and future applications,” Inf. Process. Manag., vol. 59, no. 6, p. 103061, 2022.
13. J. Cui, Y. Li, Q. Zhang, Z. He, and S. Zhao, “A Federated Learning Framework Using FedProx Algorithm for Privacy-Preserving Palmprint Recognition,” in Chinese Conference on Biometric Recognition, Springer, 2024, pp. 187–196.
14. A. El Ouadrhiri and A. Abdelhadi, “Differential privacy for deep and federated learning: A survey,” IEEE access, vol. 10, pp. 22359–22380, 2022.
15. Q. Xie et al., “Efficiency optimization techniques in privacy-preserving federated learning with homomorphic encryption: A brief survey,” IEEE Internet Things J., vol. 11, no. 14, pp. 24569–24580, 2024.
16. M. Suliman and D. Leith, “Two models are better than one: Federated learning is not private for google gboard next word prediction,” in European Symposium on Research in Computer Security, Springer, 2023, pp. 105–122.
17. S. R. Abbas, Z. Abbas, A. Zahir, and S. W. Lee, “Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration,” in Healthcare, MDPI, 2024, p. 2587.
18. C. Zhang, S. Yang, L. Mao, and H. Ning, “Anomaly detection and defense techniques in federated learning: a comprehensive review,” Artif. Intell. Rev., vol. 57, no. 6, p. 150, 2024.
19. N. Bouacida and P. Mohapatra, “Vulnerabilities in federated learning,” IEEe Access, vol. 9, pp. 63229–63249, 2021.
20. G. Xia, J. Chen, C. Yu, and J. Ma, “Poisoning attacks in federated learning: A survey,” Ieee Access, vol. 11, pp. 10708–10722, 2023.