27 citations · 47 across the 5 of their papers we have counts for
5 papers
ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices
Guangyu Zhu, Yiqin Deng, Xianhao Chen +3
Federated learning (FL) allows multiple parties (distributed devices) to train a machine learning model without sharing raw data. How to effectively and efficiently utilize the res…
Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks
Zheng Lin, Guangyu Zhu, Yiqin Deng +4
The increasingly deeper neural networks hinder the democratization of privacy-enhancing distributed learning, such as federated learning (FL), to resource-constrained devices. To o…
Actions at the Edge: Jointly Optimizing the Resources in Multi-access Edge Computing
Yiqin Deng, Xianhao Chen, Guangyu Zhu +3
Multi-access edge computing (MEC) is an emerging paradigm that pushes resources for sensing, communications, computing, storage and intelligence (SCCSI) to the premises closer to t…
From Resource Auction to Service Auction: An Auction Paradigm Shift in Wireless Networks
Xianhao Chen, Yiqin Deng, Guangyu Zhu +2
In 5G and beyond, the newly emerging services, such as edge computing/intelligence services, may demand the provision of heterogeneous communications, computing, and storage (CCS)…
Age-Stratified COVID-19 Spread Analysis and Vaccination: A Multitype Random Network Approach
Xianhao Chen, Guangyu Zhu, Lan Zhang +3
The risk for severe illness and mortality from COVID-19 significantly increases with age. As a result, age-stratified modeling for COVID-19 dynamics is the key to study how to redu…