13 citations · 39 across the 4 of their papers we have counts for
4 papers
Edge Learning with Timeliness Constraints: Challenges and Solutions
Yuxuan Sun, Wenqi Shi, Xiufeng Huang +2
Future machine learning (ML) powered applications, such as autonomous driving and augmented reality, involve training and inference tasks with timeliness requirements and are commu…
Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning
Wenqi Shi, Sheng Zhou, Zhisheng Niu +2
In federated learning (FL), devices contribute to the global training by uploading their local model updates via wireless channels. Due to limited computation and communication res…
Device Scheduling with Fast Convergence for Wireless Federated Learning
Wenqi Shi, Sheng Zhou, Zhisheng Niu
Owing to the increasing need for massive data analysis and model training at the network edge, as well as the rising concerns about the data privacy, a new distributed training fra…
Improving Device-Edge Cooperative Inference of Deep Learning via 2-Step Pruning
Wenqi Shi, Yunzhong Hou, Sheng Zhou +3
Deep neural networks (DNNs) are state-of-the-art solutions for many machine learning applications, and have been widely used on mobile devices. Running DNNs on resource-constrained…