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In this paper, we propose a transfer learning (TL)-enabled edge-CNN frameworkfor 5G industrial edge networks with privacy-preserving characteristic. Inparticular, the edge server can use the existing image dataset to train the CNNin advance, which is further fine-tuned based on the limited datasets uploadedfrom the devices. With the aid of TL, the devices that are not participating inthe training only need to fine-tune the trained edge-CNN model without trainingfrom scratch. Due to the energy budget of the devices and the limitedcommunication bandwidth, a joint energy and latency problem is formulated,which is solved by decomposing the original problem into an uploading decisionsubproblem and a wireless bandwidth allocation subproblem. Experiments usingImageNet demonstrate that the proposed TL-enabled edge-CNN framework canachieve almost 85% prediction accuracy of the baseline by uploading only about1% model parameters, for a compression ratio of 32 of the autoencoder.

A Joint Energy and Latency Framework for Transfer Learning over 5G Industrial Edge Networks