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.

翻译：远程监控系统在职业健康安全、环境监测等智能工业应用中分析环境动态。具体而言，在工业物联网系统中，海量设备数量及预期性能对计算、网络和设备能量等资源造成压力。面向智能工业物联网应用的机器/深度学习模型分布式训练，对资源受限设备在异构无线网络（HetNets）中极具挑战性。分层联邦学习（HFL）通过多层训练机制将任务卸载至邻近多接入边缘计算（MEC）单元。本文提出一种新型节能HFL框架，该框架基于无线能量传输（WET）技术，专为配备大规模多输入多输出（MIMO）无线回程的异构网络设计。我们将节能问题建模为混合整数非线性规划（MINLP）问题，优化HFL设备关联关系并管理无线传输能量。鉴于问题的高复杂度，我们设计了启发式资源管理算法H2RMA，该算法在满足能耗、信道质量及精度约束的同时保持低计算复杂度。通过高效设备调度方案进一步降低网络能耗。最后研究了设备移动性对HFL性能的影响。大量实验验证了所提资源管理方法在HFL异构网络场景中关于训练损失和电网能耗成本的高效性。