100 citations · 158 across the 15 of their papers we have counts for
6 papers · 1 filter
GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers
Liekang Zeng, Chongyu Yang, Peng Huang +3
Edge intelligence has arisen as a promising computing paradigm for supporting miscellaneous smart applications that rely on machine learning techniques. While the community has ext…
Joint Multi-User DNN Partitioning and Computational Resource Allocation for Collaborative Edge Intelligence
Xin Tang, Xu Chen, Liekang Zeng +2
Mobile Edge Computing (MEC) has emerged as a promising supporting architecture providing a variety of resources to the network edge, thus acting as an enabler for edge intelligence…
HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning
Siqi Luo, Xu Chen, Qiong Wu +2
Federated Learning (FL) has been proposed as an appealing approach to handle data privacy issue of mobile devices compared to conventional machine learning at the remote cloud with…
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
Zhi Zhou, Xu Chen, En Li +3
With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to re…
Efficient Resource Allocation for On-Demand Mobile-Edge Cloud Computing
Xu Chen, Wenzhong Li, Sanglu Lu +2
Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. Aiming at p…
Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergy
En Li, Zhi Zhou, Xu Chen
As the backbone technology of machine learning, deep neural networks (DNNs) have have quickly ascended to the spotlight. Running DNNs on resource-constrained mobile devices is, how…