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

arXiv: https://arxiv.org/abs/2104.09382 · HTML: https://mlsanity.com/paper/2104.09382/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 44 references that are in the map.
- First posted on arXiv 19 Apr 2021.

## Builds on (most cited first, 21 of 21)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Differentially Private Federated Learning: A Client Level Perspective](https://mlsanity.com/paper/1712.07557/) (2017)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning](https://mlsanity.com/paper/1602.03409/) (2016)
- [CNN-RNN: A Unified Framework for Multi-label Image Classification](https://mlsanity.com/paper/1604.04573/) (2016)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning](https://mlsanity.com/paper/1809.07857/) (2018)
- [Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions](https://mlsanity.com/paper/1904.09673/) (2019)
- [Edge Intelligence for Autonomous Driving in 6G Wireless System: Design Challenges and Solutions](https://mlsanity.com/paper/2012.06992/) (2020)
- [Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks](https://mlsanity.com/paper/1705.00704/) (2017)
- [Offloading Optimization in Edge Computing for Deep Learning Enabled Target Tracking by Internet-of-UAVs](https://mlsanity.com/paper/2008.08001/) (2020)
- [Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications](https://mlsanity.com/paper/2009.05231/) (2020)
- [Intelligent Task Offloading for Heterogeneous V2X Communications](https://mlsanity.com/paper/2006.15855/) (2020)
- [A Transfer Learning Approach for Cache-Enabled Wireless Networks](https://mlsanity.com/paper/1503.05448/) (2015)
- [Robust Deep Sensing Through Transfer Learning in Cognitive Radio](https://mlsanity.com/paper/1908.00658/) (2019)
- [Efficient Privacy Preserving Edge Computing Framework for Image Classification](https://mlsanity.com/paper/2005.04563/) (2020)

## Built on by (most cited first, 7 of 7)

- [Training Machine Learning models at the Edge: A Survey](https://mlsanity.com/paper/2403.02619/) (2024)
- [GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks](https://mlsanity.com/paper/2412.08296/) (2024)
- [Enabling All In-Edge Deep Learning: A Literature Review](https://mlsanity.com/paper/2204.03326/) (2022)
- [Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities](https://mlsanity.com/paper/2509.06968/) (2025)
- [Assessing the Performance of Analog Training for Transfer Learning](https://mlsanity.com/paper/2505.11067/) (2025)
- [Impact of L1 Batch Normalization on Analog Noise Resistant Property of Deep Learning Models](https://mlsanity.com/paper/2205.04886/) (2022)
- [Impact of Learning Rate on Noise Resistant Property of Deep Learning Models](https://mlsanity.com/paper/2205.07856/) (2022)

## Abstract (arXiv)

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.