47 citations · 132 across the 31 of their papers we have counts for
43 papers
MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehicles
Bowen Xie, Yuxuan Sun, Sheng Zhou +4
Federated learning (FL) is a promising approach to enable the future Internet of vehicles consisting of intelligent connected vehicles (ICVs) with powerful sensing, computing and c…
Over-the-Air Integrated Sensing, Communication, and Computation in IoT Networks
Xiaoyang Li, Yi Gong, Kaibin Huang +1
To facilitate the development of Internet of Things (IoT) services, tremendous IoT devices are deployed in the wireless network to collect and pass data to the server for further p…
MEET: Mobility-Enhanced Edge inTelligence for Smart and Green 6G Networks
Yuxuan Sun, Bowen Xie, Sheng Zhou +1
Edge intelligence is an emerging paradigm for real-time training and inference at the wireless edge, thus enabling mission-critical applications. Accordingly, base stations (BSs) a…
Time-Correlated Sparsification for Efficient Over-the-Air Model Aggregation in Wireless Federated Learning
Yuxuan Sun, Sheng Zhou, Zhisheng Niu +1
Federated edge learning (FEEL) is a promising distributed machine learning (ML) framework to drive edge intelligence applications. However, due to the dynamic wireless environments…
Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraints
Yuxuan Sun, Sheng Zhou, Zhisheng Niu +1
Machine learning and wireless communication technologies are jointly facilitating an intelligent edge, where federated edge learning (FEEL) is a promising training framework. As wi…
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…