While a practical wireless network has many tiers where end users do not directly communicate with the central server, the users' devices have limited computation and battery powers, and the serving base station (BS) has a fixed bandwidth. Owing to these practical constraints and system models, this paper leverages model pruning and proposes a pruning-enabled hierarchical federated learning (PHFL) in heterogeneous networks (HetNets). We first derive an upper bound of the convergence rate that clearly demonstrates the impact of the model pruning and wireless communications between the clients and the associated BS. Then we jointly optimize the model pruning ratio, central processing unit (CPU) frequency and transmission power of the clients in order to minimize the controllable terms of the convergence bound under strict delay and energy constraints. However, since the original problem is not convex, we perform successive convex approximation (SCA) and jointly optimize the parameters for the relaxed convex problem. Through extensive simulation, we validate the effectiveness of our proposed PHFL algorithm in terms of test accuracy, wall clock time, energy consumption and bandwidth requirement.

翻译：实际无线网络具有多层级结构，终端用户不直接与中心服务器通信，且用户设备计算能力与电池容量有限，服务基站（BS）的带宽固定。针对这些实际约束与系统模型，本文利用模型剪枝技术，提出了一种面向异构网络（HetNets）的剪枝增强型分层联邦学习（PHFL）算法。我们首先推导了收敛速率的上界，清晰展示了模型剪枝及客户端与关联基站间的无线通信对收敛性能的影响。随后联合优化模型剪枝率、客户端中央处理器（CPU）频率与传输功率，在严格的时延与能耗约束下最小化收敛界中的可控项。由于原始问题非凸，我们采用逐次凸近似（SCA）方法，对松弛后的凸问题参数进行联合优化。大量仿真结果表明，所提PHFL算法在测试精度、运行时间、能耗及带宽需求方面均具有显著有效性。