8 citations · 13 across the 8 of their papers we have counts for
6 papers
Data Partition and Rate Control for Learning and Energy Efficient Edge Intelligence
Xiaoyang Li, Shuai Wang, Guangxu Zhu +3
The rapid development of artificial intelligence together with the powerful computation capabilities of the advanced edge servers make it possible to deploy learning tasks at the w…
Deploying Federated Learning in Large-Scale Cellular Networks: Spatial Convergence Analysis
Zhenyi Lin, Xiaoyang Li, Vincent K. N. Lau +2
The deployment of federated learning in a wireless network, called federated edge learning (FEEL), exploits low-latency access to distributed mobile data to efficiently train an AI…
Joint Annotator-and-Spectrum Allocation in Wireless Networks for Crowd Labelling
Xiaoyang Li, Guangxu Zhu, Kaiming Shen +3
The massive sensing data generated by Internet-of-Things will provide fuel for ubiquitous artificial intelligence (AI), automating the operations of our society ranging from transp…
An Overview of Data-Importance Aware Radio Resource Management for Edge Machine Learning
Dingzhu Wen, Xiaoyang Li, Qunsong Zeng +2
The 5G network connecting billions of Internet-of-Things (IoT) devices will make it possible to harvest an enormous amount of real-time mobile data. Furthermore, the 5G virtualizat…
Wirelessly Powered Data Aggregation for IoT via Over-the-Air Functional Computation: Beamforming and Power Control
Xiaoyang Li, Guangxu Zhu, Yi Gong +1
As a revolution in networking, Internet of Things (IoT) aims at automating the operations of our societies by connecting and leveraging an enormous number of distributed devices (e…
Optimizing Wirelessly Powered Crowd Sensing: Trading energy for data
Xiaoyang Li, Changsheng You, Sergey Andreev +2
To overcome the limited coverage in traditional wireless sensor networks, \emph{mobile crowd sensing} (MCS) has emerged as a new sensing paradigm. To achieve longer battery lives o…