Personalized Federated Learning (PFL) is a new Federated Learning (FL) paradigm, particularly tackling the heterogeneity issues brought by various mobile user equipments (UEs) in mobile edge computing (MEC) networks. However, due to the ever-increasing number of UEs and the complicated administrative work it brings, it is desirable to switch the PFL algorithm from its conventional two-layer framework to a multiple-layer one. In this paper, we propose hierarchical PFL (HPFL), an algorithm for deploying PFL over massive MEC networks. The UEs in HPFL are divided into multiple clusters, and the UEs in each cluster forward their local updates to the edge server (ES) synchronously for edge model aggregation, while the ESs forward their edge models to the cloud server semi-asynchronously for global model aggregation. The above training manner leads to a tradeoff between the training loss in each round and the round latency. HPFL combines the objectives of training loss minimization and round latency minimization while jointly determining the optimal bandwidth allocation as well as the ES scheduling policy in the hierarchical learning framework. Extensive experiments verify that HPFL not only guarantees convergence in hierarchical aggregation frameworks but also has advantages in round training loss maximization and round latency minimization.

翻译：个性化联邦学习是一种新兴的联邦学习范式，特别针对移动边缘计算网络中各类移动用户设备带来的异构性问题。然而，随着用户设备数量持续增长及其带来的复杂管理需求，亟需将个性化联邦学习算法从传统的两层框架扩展为多层框架。本文提出分层个性化联邦学习算法，用于在大规模移动边缘计算网络中部署个性化联邦学习。该算法将用户设备划分为多个集群，每个集群内的用户设备同步向边缘服务器提交本地更新以进行边缘模型聚合，而各边缘服务器则半异步地将边缘模型上传至云端服务器进行全局模型聚合。上述训练方式在每轮训练损失与轮次延迟之间形成权衡关系。分层个性化联邦学习在联合优化带宽分配与边缘服务器调度策略的同时，兼顾训练损失最小化与轮次延迟最小化的双重目标。大量实验证明，该算法不仅能确保分层聚合框架的收敛性，在最大化轮次训练损失与最小化轮次延迟方面也展现出显著优势。