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.

该研究提出了层次化的个性化联邦学习算法（HPFL），将用户设备（UEs）分层聚类，从而解决移动边缘计算（MEC）网络中的异构性问题，并在全局模型与边缘模型聚合之间寻求轮数损失最小化和轮延迟最小化之间的权衡。