Federated learning and deep learning-driven adaptive cryptography for context-aware elliptic curve Diffie-Hellman optimization and anomaly-aware key management in next-generation wireless networks
*Sheela S.Corresponding authorsheela.samarth18@gmail.comSchool of Computer Science and Engineering RV UniversityBangalore, Karnataka, 560059, IndiaView full profile → , Jyothi S.jyothi.s@gat.ac.inDepartment of Computer Science and Engineering Global Academy of TechnologyBangalore, Karnataka, 560098, IndiaView full profile → , Shalini S.shalini.siddamallappa@gmail.comDepartment of Computer Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangalore, Karnataka, 560082, IndiaView full profile → , Latha Anujaplatha123@gmail.comDepartment of Information science and Engineering Dayananda Sagar College of Engineering Visvesvaraya Technological UniversityBangalore, Karnataka, 560111, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 05 Nov 2024
- Published Online:
- 30 Aug 2025
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-2431
- Pages:
- 2151–2161
Abstract
Keywords
Subject Classifications
References
[1] M. R. Barik, A. K. Das, and N. Kumar, “Security of Internet of Things (IoT) using federated learning and deep learning: A comprehensive survey,” Comput. Sci. Rev., vol. 50, pp. 100553 (2023).
[2] R. S. Sheikh, P. Gope, and S. Tanwar, “Security of federated learning with IoT systems: Issues, limitations, and future directions,” Comput. Sci. Rev., vol. 50, pp. 100549 (2023).
[3] P. S. Rana, A. S. Mandal, and A. Roy, “Hybrid and adaptive cryptographic-based secure authentication for wireless sensor networks,” J. Netw. Comput. Appl., vol. 205, pp. 103437 (2022).
[4] R. K. Jha and A. A. Ansari, “Security for wireless sensor networks using cryptography,” Mater. Today: Proc., vol. 83, pp. 1607–1613 (2023).
[5] A. Mukherjee, “Physical Layer Security 2.0: Emerging Wireless Security Solutions for Future IoT,” in IEEE World Forum on Internet of Things (WF-IoT) (2023).
[6] D. Halder and A. Ukil, “Federated learning for IoT devices: Enhancing TinyML with on-device training,” Future Gener. Comput. Syst., vol. 150, pp. 222–234 (2023).
[7] B. R. Subbaiah, K. R. Prasad, R. O. Reddy, K. Soma, M. Yaramadhi, E. Muniyandy, and K. Palaparthy, “Innovating cybersecurity: The evolution of zero trust architecture amid emerging technological paradigms,” J. Inf. Optim. Sci., vol. 46, no. 2 (2025).
[8] S. Sharma, M. S. Obaidat, and V. Sharma, “A physical layer security scheme for 6G wireless networks using deep learning,” Comput. Commun., vol. 214, pp. 51–60 (2024).
[9] H. Sun, Y. Zhang, and L. Zhou, “Quantum-empowered federated learning and 6G wireless networks: A comprehensive survey,” Future Gener. Comput. Syst., vol. 155, pp. 775–795 (2024).
[10] G. R. Koteswara Rao, H. M. A. Ghanimi, V. Ramachandran, D. Al-Qahtani, P. Dadheech, and S. Sengan, “Enhanced security in federated learning by integrating homomorphic encryption for privacy-protected, collaborative model training,” J. Discret. Math. Sci. Cryptogr., vol. 27, no. 2-A (2024).
[11] X. Liu, H. Wang, and Y. Shi, “Deep learning based physical layer security for terrestrial wireless communication,” Phys. Commun., vol. 57, pp. 101020 (2023).
[12] Y. Li, H. Jiang, and Q. Ren, “Federated learning-based zero trust intrusion detection system for Industrial IoT,” Comput. Commun., vol. 223, pp. 89–98 (2024).
