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

• URL: http://arxiv.org/abs/2104.09382v1
• Date: Mon, 19 Apr 2021 15:13:16 GMT
• Title: A Joint Energy and Latency Framework for Transfer Learning over 5G Industrial Edge Networks
• Authors: Bo Yang, Omobayode Fagbohungbe, Xuelin Cao, Chau Yuen, Lijun Qian, Dusit Niyato, and Yan Zhang
• Abstract summary: We propose a transfer learning-enabled edge-CNN framework for 5G industrial edge networks. In particular, the edge server can use the existing image dataset to train the CNN in advance. 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.
• Score: 53.26338041079138
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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