In this article, we propose a transfer learning (TL) enabled edge convolutional neural network (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.

中文翻译：

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用于 5G 工业边缘网络迁移学习的联合能量和延迟框架


在本文中，我们提出了一种支持迁移学习（TL）的边缘卷积神经网络（CNN）框架，用于具有隐私保护特性的 5G 工业边缘网络。特别是，边缘服务器可以使用现有的图像数据集提前训练CNN，并根据设备上传的有限数据集进一步进行微调。借助TL，不参与训练的设备只需对训练好的edge-CNN模型进行微调，无需从头开始训练。由于设备的能量预算和有限的通信带宽，提出了联合能量和延迟问题，通过将原始问题分解为上传决策子问题和无线带宽分配子问题来解决。使用 ImageNet 的实验表明，对于自动编码器的压缩比为 32，所提出的支持 TL 的边缘 CNN 框架仅通过上传约 1% 的模型参数即可实现近 85% 的基线预测精度。