FEDERATED LEARNING FOR SECURE AND PRIVACY-AWARE AI IN NEXT-GENERATION TELECOM NETWORKS
Keywords:
Federated Learning, User Privacy, AI in Telecom, Secure Aggregation, GDPR Compliance, Edge Intelligence, Differential PrivacyAbstract
As artificial intelligence becomes central to telecom service optimization and personalization, ensuring the privacy of user data is a growing challenge. Traditional centralized machine learning methods require raw data aggregation, exposing sensitive information and risking regulatory violations. This paper presents a federated learning (FL) approach tailored for telecom environments, enabling AI model training directly on distributed user devices without transferring personal data to central servers. The proposed system integrates differential privacy and secure aggregation mechanisms to enhance protection while preserving model performance. Experimental evaluations using synthetic mobile usage data demonstrate that our FL models achieve up to 96% of the accuracy of centralized baselines, while significantly reducing privacy leakage risks. The results confirm that federated learning is a scalable, privacy-preserving solution for AI-driven telecom services that aligns with global data protection standards.
References
1. H. 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. Artificial Intelligence and Statistics (AISTATS), 2017, pp. 1273–1282.
2. J. Konecny, H. B. McMahan, F. X. Yu, P. Richtarik, A. T. Suresh, and D. Bacon, "Federated learning: Strategies for improving communication efficiency," arXiv preprint arXiv:1610.05492, 2016.
3. N. D. Lane, A. Bhattacharya, S. Georgiev, and P. K. Ravi, "An early resource characterization of deep learning on wearables, smartphones and Internet-of-Things devices," in Proc. 2015 Int. Conf. Internet of Things Design and Implementation (IoTDI), 2015, pp. 1–10.
4. R. Shokri and V. Shmatikov, "Privacy-preserving deep learning," in Proc. 22nd ACM SIGSAC Conf. Computer and Communications Security (CCS), 2015, pp. 1310–1321.
5. C. Dwork, A. Roth, "The algorithmic foundations of differential privacy," Foundations and Trends® in Theoretical Computer Science, vol. 9, no. 3–4, pp. 211–407, 2014.
6. K. Bonawitz et al., "Practical secure aggregation for privacy-preserving machine learning," in Proc. ACM SIGSAC Conf. Computer and Communications Security (CCS), 2017, pp. 1175–1191.
7. S. Wang, T. Tuor, T. Salonidis, K. Leung, C. Makaya, T. He, and K. Chan, "Adaptive federated learning in resource constrained edge computing systems," IEEE Journal on Selected Areas in Communications, vol. 37, no. 6, pp. 1205–1221, Jun. 2019.
8. Google AI Blog, "Federated Learning: Collaborative Machine Learning without Centralized Training Data," Apr. 2017. [Online]. Available: https://ai.googleblog.com/2017/04/federated-learning-collaborative.html
9. G. Zhu, D. Liu, Y. Du, C. You, J. Zhang, and K. Huang, "Towards federated learning in 6G: A survey," IEEE Internet of Things Journal, vol. 8, no. 22, pp. 15784–15816, 2021.
10. European Commission, "General Data Protection Regulation (GDPR)," 2016. [Online]. Available: https://gdpr.eu/
