Hierarchical Federated Learning Framework for Privacy-Enhanced RAN Optimization in Distributed 5G and Private LTE Systems
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V6I3P101Keywords:
Federated Learning, 5G, LTE, Radio Access Network (RAN) Optimization, Private LTE, Edge ComputingAbstract
This paper presents Hierarchical FL-RAN, a novel federated learning framework for privacy-preserving Radio Access Network (RAN) optimization in distributed 5G and private LTE systems. By leveraging a multi-tier aggregation approach, local models are trained at edge RAN nodes and aggregated progressively through intermediate controllers and central servers, reducing communication overhead and enhancing scalability. The framework integrates domain-specific feature encoding with temporal filtering to capture key network KPIs such as interference patterns and handover metrics while ensuring data privacy. Simulation results demonstrate faster model convergence and improved resource efficiency compared to conventional federated learning methods. The proposed framework enables secure, real-time, and distributed intelligence for RAN optimization in heterogeneous, multi-tenant wireless networks
References
[1] Distributed machine learning for wireless communication networks: Techniques, architectures, and applications,” IEEE Communications Surveys & Tutorials, vol. 22, no. 4, pp. 2152–2185, 4th Quart., 2020. doi:10.1109/COMST.2020.2986022
[2] S. Wang, T. Tuor, T. Salonidis, K. Leung, C. Makaya, T. He, and K. Chan, “When edge meets learning: Adaptive control for resource-constrained distributed machine learning,” IEEE INFOCOM, 2018, pp. 63–71.
[3] H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” Proc. 20th Int. Conf. Artificial Intelligence and Statistics (AISTATS), 2017, pp. 1273–1282.
[4] J. Konečný, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon, “Federated learning: Strategies for improving communication efficiency,” arXiv preprint arXiv:1610.05492, 2016.
[5] W. Y. B. Lim, N. C. Luong, D. T. Hoang, Y. Jiao, Y.-C. Liang, Q. Yang, and D. Niyato, “Federated learning in mobile edge networks: A comprehensive survey,” IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 2031–2063, 3rd Quart., 2020. doi: 10.1109/COMST.2020.2986024
[6] R. Shokri and V. Shmatikov, “Privacy-preserving deep learning,” Proc. 22nd ACM SIGSAC Conf. on Computer and Communications Security, 2015, pp. 1310–1321.
[7] O. S. Alsheikh, D. Niyato, S. Lin, H. P. Tan, and Z. Han, “Machine learning in wireless sensor networks: Algorithms, strategies, and applications,” IEEE Communications Surveys & Tutorials, vol. 16, no. 4, pp. 1996–2018, Fourth Quarter 2014.
[8] K. Bonawitz et al., “Towards federated learning at scale: System design,” Proc. 2nd SysML Conf., 2019.
[9] C. Ma, J. Li, M. Ding, H. H. Yang, F. Shu, T. Q. S. Quek, and H. V. Poor, “On safeguarding privacy and security in the framework of federated learning,” IEEE Network, vol. 34, no. 4, pp. 242–248, Jul./Aug. 2020. doi: 10.1109/MNET.001.1900506
[10] Y. Liu, M. Chen, W. Saad, M. Shikh-Bahaei, and H. V. Poor, “Client selection for federated learning with heterogeneous resources in mobile edge computing,” IEEE Transactions on Wireless Communications, vol. 19, no. 4, pp. 2446–2459, Apr. 2020.
[11] C. Dwork, “Differential privacy,” in Automata, Languages and Programming, Lecture Notes in Computer Science, vol. 4052, Springer, 2006, pp. 1–12.
[12] K. Bonawitz et al., “Practical secure aggregation for privacy-preserving machine learning,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 2017, pp. 1175–1191.
[13] I. Mironov, “Rényi Differential Privacy,” in Proc. 2017 IEEE 30th Computer Security Foundations Symposium (CSF), Santa Barbara, CA, USA, Aug. 2017, pp. 263–275. doi: 10.1109/CSF.2017.11
[14] K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth, “Practical Secure Aggregation for Privacy-Preserving Machine Learning,” in Proc. 2017 ACM SIGSAC Conf. on Computer and Communications Security (CCS), Dallas, TX, USA, Oct. 2017, pp. 1175–1191. doi: 10.1145/3133956.3133982
[15] A. Geyer, T. Klein, and M. Nabi, “Differentially Private Federated Learning: A Client Level Perspective,” arXiv preprint arXiv:1712.07557, 2017.
[16] P. Blanchard, E. M. El Mhamdi, R. Guerraoui, and J. Stainer, “Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent,” in Proceedings of the 31st International Conference on Neural Information Processing Systems (NeurIPS), 2017, pp. 119–129.
[17] M. Mohri, G. Sivek, and A. T. Suresh, “Agnostic Federated Learning,” in Proceedings of the 36th International Conference on Machine Learning (ICML), 2019, pp. 4615–4625.
[18] Device Association for RAN Slicing Based on Hybrid Federated Deep Reinforcement Learning,” IEEE Trans. Veh. Tech., vol. 69, no. 12, pp. 15731–15745, Dec. 2020. doi: 10.1109/TVT.2020.3033035
[19] M. Chen, U. Challita, W. Saad, C. Yin, and M. Debbah, “Artificial Intelligence for Wireless Networks: A Tutorial,” IEEE Communications Surveys & Tutorials, vol. 22, no. 2, pp. 1671–1686, Second Quarter 2020.
[20] T. Nishio and R. Yonetani, “Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge,” in Proc. IEEE ICC, Shanghai, China, May 2019, pp. 1–7.
[21] K. Yang, Y. Shi, and Z. Ding, “Federated Learning for Intelligent Wireless Networks: Recent Advances and Future Directions,” IEEE Wireless Communications, vol. 28, no. 3, pp. 64–71, Jun. 2021.
[22] S. Kairouz et al., “Advances and Open Problems in Federated Learning,” Foundations and Trends® in Machine Learning, vol. 14, no. 1–2, pp. 1–210, 2021.
[23] L. Liu, J. Kang, Y. Fu, and J. Wu, “Byzantine-robust Federated Learning: A Survey,” IEEE Transactions on Neural Networks and Learning Systems, 2023.
[24] H. B. McMahan and D. Ramage, “Federated Learning: Collaborative Machine Learning without Centralized Training Data,” Google Research Blog, 2017.
[25] P. Kairouz, H. B. McMahan et al., “Federated Learning: Challenges, Methods, and Future Directions,” IEEE Signal Processing Magazine, vol. 37, no. 3, pp. 50-60, 2020.
[26] A. Khalid, M. S. Alkahtani, and M. A. Khan, “A survey of federated learning in edge computing for intelligent IoT: Recent advances and future directions,” Frontiers in Computer Science, vol. 6, Art. no. 1494174, 2024. [Online]. Available: https://www.frontiersin.org/articles/10.3389/fcomp.2024.1494174/full
[27] M. Salehi Heydar Abad, E. Ozfatura, D. Gündüz, and Ö. Ercetin, “Hierarchical federated learning across heterogeneous cellular networks,” in *Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP)*, Barcelona, Spain, May 2020, pp. 8866–8870.
[28] O. Aygün, M. Kazemi, D. Gündüz, and T. M. Duman, “Hierarchical over‑the‑air federated edge learning,” in *Proc. IEEE Int. Conf. Commun. (ICC)*, Seoul, South Korea, May 2022, pp. 3376–3381.
[29] W. Fang, D.-J. Han, and C. G. Brinton, “Submodel partitioning in hierarchical federated learning: Algorithm design and convergence analysis,” *arXiv*, Oct. 2023. [Online]. Available: https://arxiv.org/abs/2310.17890 , preprint
[30] Y. Shi, M. Chen, et al., “Cloud‑RAN vertical federated learning under fronthaul constraints,” *arXiv*, May 2023. [Online]. Available: https://arxiv.org/abs/2305.06279, preprint
[31] S. K. Sharma, S. Chatzinotas, and B. Ottersten, “Over-the-Air Federated Learning for Vehicular Networks: A Survey,” IEEE Transactions on Vehicular Technology, vol. 71, no. 4, pp. 3773–3793, Apr. 2022. DOI: 10.1109/TVT.2021.3130408
[32] A. N. Bhagoji, S. Chakraborty, P. Mittal, and S. Mittal, “Communication-Efficient Federated Learning with Hierarchical Clustered Aggregation,” IEEE Transactions on Communications, vol. 69, no. 3, pp. 1613–1626, Mar. 2021. DOI: 10.1109/TCOMM.2020.3031456
[33] Noor, S., AlQahtani, S. A., & Khan, S. (2025). Chronic liver disease detection using ranking and projection-based feature optimization with deep learning. AIMS Bioengineering, 12(1).
[34] Lakshmikanthan, G. (2022). EdgeChain Health: A Secure Distributed Framework for Next-Generation Telemedicine. International Journal of AI, BigData, Computational and Management Studies, 3(1), 32-36.
