Mobile edge learning is an emerging technique that enables distributed edge
devices to collaborate in training shared machine learning models by exploiting
their local data samples and communication and computation resources. To deal
with the straggler dilemma issue faced in this technique, this paper proposes a
new device to device enabled data sharing approach, in which different edge
devices share their data samples among each other over communication links, in
order to properly adjust their computation loads for increasing the training
speed. Under this setup, we optimize the radio resource allocation for both
data sharing and distributed training, with the objective of minimizing the
total training delay under fixed numbers of local and global iterations.
Numerical results show that the proposed data sharing design significantly
reduces the training delay, and also enhances the training accuracy when the
data samples are non independent and identically distributed among edge
devices.

本文提出使用不同边缘设备间通过通信链接共享数据样本的新设备到设备的数据共享方法，在此设置下，优化了无线电资源分配以减少训练时延，并在数据样本在边缘设备之间非独立且非同分布时增加了训练精度。