1 citations · 2 across the 5 of their papers we have counts for
5 papers
When Computing Power Network Meets Distributed Machine Learning: An Efficient Federated Split Learning Framework
Xinjing Yuan, Lingjun Pu, Lei Jiao +3
In this paper, we advocate CPN-FedSL, a novel and flexible Federated Split Learning (FedSL) framework over Computing Power Network (CPN). We build a dedicated model to capture the…
An Edge Computing-based Photo Crowdsourcing Framework for Real-time 3D Reconstruction
Shuai Yu, Xu Chen, Shuai Wang +2
Image-based three-dimensional (3D) reconstruction utilizes a set of photos to build 3D model and can be widely used in many emerging applications such as augmented reality (AR) and…
Cost-efficient and Skew-aware Data Scheduling for Incremental Learning in 5G Network
Lingjun Pu, Xinjing Yuan, Xiaohang Xu +3
To facilitate the emerging applications in 5G networks, mobile network operators will provide many network functions in terms of control and prediction. Recently, they have recogni…
Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing
Xu Chen, Lingjun Pu, Lin Gao +2
In this article we propose a novel Device-to-Device (D2D) Crowd framework for 5G mobile edge computing, where a massive crowd of devices at the network edge leverage the network-as…
Content Retrieval At the Edge: A Social-aware and Named Data Cooperative Framework
Lingjun Pu, Xu Chen, Jingdong Xu +1
Recent years with the popularity of mobile devices have witnessed an explosive growth of mobile multimedia contents which dominate more than 50\% of mobile data traffic. This signi…