  In this paper, we propose a transfer learning (TL)-enabled edge-CNN framework
for 5G industrial edge networks with privacy-preserving characteristic. In
particular, the edge server can use the existing image dataset to train the CNN
in advance, which is further fine-tuned based on the limited datasets uploaded
from the devices. With the aid of TL, the devices that are not participating in
the training only need to fine-tune the trained edge-CNN model without training
from scratch. Due to the energy budget of the devices and the limited
communication bandwidth, a joint energy and latency problem is formulated,
which is solved by decomposing the original problem into an uploading decision
subproblem and a wireless bandwidth allocation subproblem. Experiments using
ImageNet demonstrate that the proposed TL-enabled edge-CNN framework can
achieve almost 85% prediction accuracy of the baseline by uploading only about
1% model parameters, for a compression ratio of 32 of the autoencoder.
