Optimal Resource Management for Hierarchical Federated Learning over HetNets with Wireless Energy Transfer

Authors

Rami Hamdi, University of Aberdeen
Ahmed Ben Said, Qatar University
Emna Baccour, Hamad Bin Khalifa University, College of Science and Engineering
Aiman Erbad, Hamad Bin Khalifa University, College of Science and Engineering
Amr Mohamed, Qatar University
Mounir Hamdi, Hamad Bin Khalifa University, College of Science and Engineering
Mohsen Guizani, Mohamed Bin Zayed University of Artificial IntelligenceFollow

Document Type

Article

Publication Title

IEEE Internet of Things Journal

Abstract

Remote monitoring systems analyze the environment dynamics in different smart industrial applications, such as occupational health and safety, and environmental monitoring. Specifically, in industrial Internet of Things (IoT) systems, the huge number of devices and the expected performance put pressure on resources, such as computational, network, and device energy. Distributed training of Machine and Deep Learning (ML/DL) models for intelligent industrial IoT applications is very challenging for resource limited devices over heterogeneous wireless networks (HetNets). Hierarchical Federated Learning (HFL) performs training at multiple layers offloading the tasks to nearby Multi-Access Edge Computing (MEC) units. In this paper, we propose a novel energy-efficient HFL framework enabled by Wireless Energy Transfer (WET) and designed for heterogeneous networks with massive Multiple-Input Multiple-Output (MIMO) wireless backhaul. Our energy-efficiency approach is formulated as a Mixed-Integer Non-Linear Programming (MINLP) problem, where we optimize the HFL device association and manage the wireless transmitted energy. However due to its high complexity, we design a Heuristic Resource Management Algorithm, namely H2RMA, that respects energy, channel quality, and accuracy constraints, while presenting a low computational complexity. We also improve the energy consumption of the network using an efficient device scheduling scheme. Finally, we investigate device mobility and its impact on the HFL performance. Our extensive experiments confirm the high performance of the proposed resource management approach in HFL over HetNets, in terms of training loss and grid energy costs.

First Page

1

Last Page

1

DOI

10.1109/JIOT.2023.3271692

Publication Date

5-1-2023

Keywords

Convergence, device association, Energy consumption, energy efficiency, Federated learning, HetNets, Hierarchical federated learning, Performance evaluation, Resource management, Task analysis, Training, wireless energy transfer

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Recommended Citation

R. Hamdi et al., "Optimal Resource Management for Hierarchical Federated Learning over HetNets with Wireless Energy Transfer," in IEEE Internet of Things Journal, May 2023. doi: 10.1109/JIOT.2023.3271692.