A novel cryptographic approach to evaluate the security of federated learning frameworks
*V. Dankan GowdaCorresponding authordankan.v@bmsit.inDepartment of Electronics and Communication EngineeringBMS Institute of Technology and ManagementBangalore, Karnataka, IndiaView full profile → , Sadashiva V. Chakrasalisadashiva.c@msrit.eduDepartment of Electronics and Communication EngineeringMSR Nagar MSRIT PostM S Ramaiah Institute of TechnologyBengaluru, Karnataka, IndiaView full profile → , Pullela SVVSR Kumarpullelark@yahoo.comDepartment of Computer Science & EngineeringAditya UniversitySurampalem, Andhra Pradesh, IndiaView full profile → , Sampada Abhijit Dholesampada.dhole@bharatividyapeeth.eduDepartment of Electronic and TelecommunicationBharati Vidyapeeth College of Engineering for WomenPune, Maharashtra, IndiaView full profile → , Jayamala Kumar Patiljayamala.p@rediffmail.comDepartment of Electronics and Telecommunication EngineeringBharati Vidyapeeth’s College of EngineeringKolhapur, Maharashtra, IndiaView full profile → , Shivoham Singhshivohamsingh@gmail.comDepartment of OperationsSymbiosis International (Deemed to be University)Symbiosis Institute of Business ManagementPune, Hyderabad, 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-2151
- Pages:
- 1527–1536
Abstract
Keywords
Subject Classifications
References
[1] H. Li, K. Ota and M. Dong, “Learning IoT in edge: Deep learning for the Internet of Things with edge computing,” IEEE Network, vol. 32, no. 1, pp. 96-101 (2018).
[2] P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings, R. G. Rajagopal, M. Gruteser, Z. Harchaoui, C. He, L. Huo, P. Jaggi, T. Javidi, G. Joshi, M. Khodak, J. Konecny, A. Korolova, F. Koushanfar, S. Koyejo, T. Li, M. Liebenwein, D. Mazières, H. Brendan McMahan, B. McMahan, A. Nedic, D. Ramage, R. Singh, K. Sridharan, A. T. Suresh, F. Tramer, and M. Zinkevich, “Advances and open problems in federated learning,” Foundations Trends® Mach. Learn., vol. 14, no. 1-2, pp. 1-210 (2021).
[3] N. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth, “Practical secure aggregation for federated learning on user-held data,” in Proc. 2017 ACM SIGSAC Conf. Comput. Commun. Secur. (CCS ’17), pp. 1175-1191 (2017).
[4] Veera Sivakumar, “A novel RF-SMOTE model to enhance the definite apprehensions for IoT security attacks,” J. Discrete Math. Sci. Cryptogr., vol. 26, no. 3, pp. 861-873 (2023).
[5] A. Chaturvedi, “Securing networked image transmission using public-key cryptography and identity authentication,” J. Discrete Math. Sci. Cryptogr., vol. 26, no. 3, pp. 779-791 (2023).
[6] B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Proc. 20th Int. Conf. Artif. Intell. Stat. (AISTATS), vol. 54, pp. 1273-1282 (2017).
[7] C. Dwork and A. Roth, “The algorithmic foundations of differential privacy,” Foundations Trends® Theor. Comput. Sci., vol. 9, no. 3-4, pp. 211-407 (2014).
[8] R. Shokri and V. Shmatikov, “Privacy-preserving deep learning,” in Proc. 2015 ACM SIGSAC Conf. Comput. Commun. Secur. (CCS ’15), pp. 1310-1321 (2015).
[9] A. G. Roy and S. Siddiqui, “Federated learning for brain tumour segmentation,” in Proc. IEEE Int. Conf. Image Process. (ICIP), pp. 1414-1418 (2020).
[10] J. Su, A. Elgabli, M. Salim, S. Wang, X. Chen, and M. Alouini, “Securing model sharing in federated learning,” IEEE Internet Things J., vol. 8, no. 7, pp. 5044-5054 (2021).
[11] G. Manivasagam and K. D. V. Prasad, “Novel approaches to biometric security using enhanced session keys and elliptic curve cryptography,” J. Discrete Math. Sci. Cryptogr., vol. 27, no. 2-A, pp. 477–488 (2024).
[12] R. Senthil Kumar and K. D. V. Prasad, “Securing digital authentication with cryptographic innovations in intrinsic verification systems,” J. Discrete Math. Sci. Cryptogr., vol. 27, no. 2-A, pp. 317–328 (2024).
[13] K. D. V. Prasad and T. Thomas, “Cryptographic image-based data security strategies in wireless sensor networks,” J. Discrete Math. Sci. Cryptogr., vol. 27, no. 2-A, pp. 293–304 (2024).
[14] P. Sarma and M. Rahman, “Mathematical analysis of wavelet-based multi-image compression in medical diagnostics,” J. Discrete Math. Sci. Cryptogr., vol. 27, no. 2-B, pp. 675–687 (2024).
[15] A. Kumar, “A novel approach of unsupervised feature selection using iterative shrinking and expansion algorithm,” J. Interdiscip. Math., vol. 26, no. 3, pp. 519-530 (2023).
[16] K. Raghavendra, “Vector space modelling-based intelligent binary image encryption for secure communication,” J. Discrete Math. Sci. Cryptogr., vol. 25, no. 4, pp. 1157-1171 (2022).
[17] M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS ’16), Vienna, Austria, pp. 308–318, Oct. 2016.
[18] L. Liu, J. Zhang, S. Song, and K. B. Letaief, “Federated learning in the sky: Joint power allocation and scheduling with UAV swarms,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 6, pp. 2312–2325, Jun. 2021.
