Collaborative inference systems are one of the emerging solutions for
deploying deep neural networks (DNNs) at the wireless network edge. Their main
idea is to divide a DNN into two parts, where the first is shallow enough to be
reliably executed at edge devices of limited computational power, while the
second part is executed at an edge server with higher computational
capabilities. The main advantage of such systems is that the input of the DNN
gets compressed as the subsequent layers of the shallow part extract only the
information necessary for the task. As a result, significant communication
savings can be achieved compared to transmitting raw input samples. In this
work, we study early exiting in the context of collaborative inference, which
allows obtaining inference results at the edge device for certain samples,
without the need to transmit the partially processed data to the edge server at
all, leading to further communication savings. The central part of our system
is the transmission-decision (TD) mechanism, which, given the information from
the early exit, and the wireless channel conditions, decides whether to keep
the early exit prediction or transmit the data to the edge server for further
processing. In this paper, we evaluate various TD mechanisms and show
experimentally, that for an image classification task over the wireless edge,
proper utilization of early exits can provide both performance gains and
significant communication savings.

协作推理系统是在无线网络边缘部署深度神经网络的新兴解决方案之一。本研究工作通过评估不同的传输决策机制，实验证明在无线边缘上适当利用早期退出可以在图像分类任务中提供性能增益和显著的通信节省。

噪声无线信道上的自适应提前退出协同推理

Adaptive Early Exiting for Collaborative Inference over Noisy Wireless  Channels

We study federated machine learning at the wireless network edge, where
limited power wireless devices, each with its own dataset, build a joint model
with the help of a remote parameter server (PS). We consider a
bandwidth-limited fading multiple access channel (MAC) from the wireless
devices to the PS, and propose various techniques to implement distributed
stochastic gradient descent (DSGD). We first propose a digital DSGD (D-DSGD)
scheme, in which one device is selected opportunistically for transmission at
each iteration based on the channel conditions; the scheduled device quantizes
its gradient estimate to a finite number of bits imposed by the channel
condition, and transmits these bits to the PS in a reliable manner. Next,
motivated by the additive nature of the wireless MAC, we propose a novel analog
communication scheme, referred to as the compressed analog DSGD (CA-DSGD),
where the devices first sparsify their gradient estimates while accumulating
error, and project the resultant sparse vector into a low-dimensional vector
for bandwidth reduction. Numerical results show that D-DSGD outperforms other
digital approaches in the literature; however, in general the proposed CA-DSGD
algorithm converges faster than the D-DSGD scheme and other schemes in the
literature, and reaches a higher level of accuracy. We have observed that the
gap between the analog and digital schemes increases when the datasets of
devices are not independent and identically distributed (i.i.d.). Furthermore,
the performance of the CA-DSGD scheme is shown to be robust against imperfect
channel state information (CSI) at the devices. Overall these results show
clear advantages for the proposed analog over-the-air DSGD scheme, which
suggests that learning and communication algorithms should be designed jointly
to achieve the best end-to-end performance in machine learning applications at
the wireless edge.

本文研究了在无线网络边缘的联邦机器学习，其中有限功率的无线设备，每个设备具有自己的数据集，并利用远程参数服务器（PS）建立联合模型。文章提出了各种技术来实现分布式随机梯度下降（DSGD），其中包括数字 DSGD（D-DSGD）和压缩模拟 DSGD（CA-DSGD）算法，并通过实验数据表明 CA-DSGD 算法收敛速度更快，达到更高的精度。