We study federated edge learning, where a global model is trained
collaboratively using privacy-sensitive data at the edge of a wireless network.
A parameter server (PS) keeps track of the global model and shares it with the
wireless edge devices for training using their private local data. The devices
then transmit their local model updates, which are used to update the global
model, to the PS. The algorithm, which involves transmission over PS-to-device
and device-to-PS links, continues until the convergence of the global model or
lack of any participating devices. In this study, we consider device selection
based on downlink channels over which the PS shares the global model with the
devices. Performing digital downlink transmission, we design a partial device
participation framework where a subset of the devices is selected for training
at each iteration. Therefore, the participating devices can have a better
estimate of the global model compared to the full device participation case
which is due to the shared nature of the broadcast channel with the price of
updating the global model with respect to a smaller set of data. At each
iteration, the PS broadcasts different quantized global model updates to
different participating devices based on the last global model estimates
available at the devices. We investigate the best number of participating
devices through experimental results for image classification using the MNIST
dataset with biased distribution.

本文研究联邦边缘学习在无线网络边缘使用隐私数据协作训练全局模型，通过数字下行传输，设计了一个部分设备参与的框架和设备选择方法，实验结果表明最佳参与设备数量取决于统计偏差的 MNIST 数据集。