Energy-Efficient Hierarchical Federated Learning in Massive Wireless IoT Networks

• Renan R. de Oliveira UFG / IFG http://orcid.org/0009-0002-7806-4381
• Kleber V. Cardoso UFG https://orcid.org/0000-0001-5152-5323
• Antonio Oliveira-Jr UFG / Fraunhofer Portugal AICOS https://orcid.org/0000-0003-3595-3525

DOI: https://doi.org/10.5753/sbrc.2025.5906

Resumo

Federated Learning (FL) enables collaborative model training without sharing raw data, preserving privacy and reducing communication overhead. On the other hand, in Internet of Things (IoT) wireless networks, FL faces issues such as limited resources, unreliable communication channels, and large delays. Hierarchical Federated Learning (HFL) addresses these issues using a tree topology with intermediate servers to reduce communication distances, improve aggregation efficiency, and mitigate transmission failures. However, current algorithms are not well-suited to address the scalability challenges posed by the massive scale of beyond-5G networks. In this context, we propose a novel HFL algorithm called HFLwOpt, which dynamically optimizes communication and computation resources in massive wireless IoT networks, maximizing successful transmissions, minimizing energy use, and reducing training latency. Our simulation with over 1, 000 devices, utilizing three levels of aggregation, demonstrates that HFLwOpt surpasses baselines with fixed resource allocations. The results reveal a reduction of up to 45.34% in energy efficiency and 77.01% in training latency for the MNIST-based dataset, and 42.23% in energy efficiency and 76.77% in training latency for the FMNIST-based dataset.

Palavras-chave: Hierarchical Federated Learning, Wireless IoT Networks, Resource Allocation, Linear Programming, Energy Efficiency

Referências

Chen, M., Yang, Z., Saad, W., Yin, C., Poor, H. V., & Cui, S. (2021). A joint learning and communications framework for federated learning over wireless networks. IEEE Transactions on Wireless Communications, 20(1), 269–283.

Cho, Y. J., Wang, J., & Joshi, G. (2020). Client selection in federated learning: Convergence analysis and power-of-choice selection strategies. CoRR, abs/2010.01243.

ETSI. (2022). Multi-access edge computing (MEC); Framework and reference architecture. ETSI GS MEC 003 V3.1.1.

Liu, L., Zhang, J., Song, S., & Letaief, K. B. (2020). Client-edge-cloud hierarchical federated learning. In ICC 2020 - 2020 IEEE International Conference on Communications (ICC) (pp. 1–6).

Luo, S., Chen, X., Wu, Q., Zhou, Z., & Yu, S. (2020). HFEL: Joint edge association and resource allocation for cost-efficient hierarchical federated edge learning. IEEE Transactions on Wireless Communications, 19(10), 6535–6548.

McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2016). Federated learning of deep networks using model averaging. CoRR, abs/1602.05629.

Nakayama, K., & Jeno, G. (2022). Federated learning with Python: Design and implement a federated learning system and develop applications using existing frameworks. Packt Publishing.

Poularakis, K., Llorca, J., Tulino, A. M., Taylor, I., & Tassiulas, L. (2020). Service placement and request routing in MEC networks with storage, computation, and communication constraints. IEEE/ACM Transactions on Networking, 28(3), 1047–1060.

Simos, M., Bouzinis, P. S., Diamantoulakis, P. D., Sarigiannidis, P., & Karagiannidis, G. K. (2022). Hierarchical federated learning for the next generation IoT. In 2022 18th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) (pp. 198–203).

Stergiou, K. D., & Psannis, K. E. (2022). Federated learning approach decouples clients from training a local model and with the communication with the server. IEEE Transactions on Network and Service Management, 19(4), 4213–4218.

Su, L., Zhou, R., Wang, N., Chen, J., & Li, Z. (2024). Low-latency hierarchical federated learning in wireless edge networks. IEEE Internet of Things Journal, 11(4), 6943–6960.

Wu, J., Dong, F., Leung, H., Zhu, Z., Zhou, J., & Drew, S. (2024). Topology-aware federated learning in edge computing: A comprehensive survey. ACM Computing Surveys, 56(10).

Xu, B., Xia, W., Wen, W., Liu, P., Zhao, H., & Zhu, H. (2022). Adaptive hierarchical federated learning over wireless networks. IEEE Transactions on Vehicular Technology, 71(2), 2070–2083.

Yang, Z., Chen, M., Wong, K.-K., Poor, H. V., & Cui, S. (2022). Federated learning for 6G: Applications, challenges, and opportunities. Engineering, 8, 33–41.

You, C., Guo, K., Yang, H. H., & Quek, T. Q. S. (2023). Hierarchical personalized federated learning over massive mobile edge computing networks. IEEE Transactions on Wireless Communications, 22(11), 8141–8157.