Federated Learning (FL) enables training a global model without sharing the
decentralized raw data stored on multiple devices to protect data privacy. Due
to the diverse capacity of the devices, FL frameworks struggle to tackle the
problems of straggler effects and outdated models. In addition, the data
heterogeneity incurs severe accuracy degradation of the global model in the FL
training process. To address aforementioned issues, we propose a hierarchical
synchronous FL framework, i.e., FedHiSyn. FedHiSyn first clusters all available
devices into a small number of categories based on their computing capacity.
After a certain interval of local training, the models trained in different
categories are simultaneously uploaded to a central server. Within a single
category, the devices communicate the local updated model weights to each other
based on a ring topology. As the efficiency of training in the ring topology
prefers devices with homogeneous resources, the classification based on the
computing capacity mitigates the impact of straggler effects. Besides, the
combination of the synchronous update of multiple categories and the device
communication within a single category help address the data heterogeneity
issue while achieving high accuracy. We evaluate the proposed framework based
on MNIST, EMNIST, CIFAR10 and CIFAR100 datasets and diverse heterogeneous
settings of devices. Experimental results show that FedHiSyn outperforms six
baseline methods, e.g., FedAvg, SCAFFOLD, and FedAT, in terms of training
accuracy and efficiency.

本文提供了一种基于异步联邦学习的分层同步 FL 框架：FedHiSyn，首先将设备基于其计算能力分成少量的几类，然后在这些类别中通过同步更新和设备间通信相结合的方式解决了异质性数据和呆滞时间等问题，实验结果表明在精度与效率方面的表现均优于其他六种基线方法。