172 citations · 1.4k across the 92 of their papers we have counts for
6 papers · 1 filter
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…
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…
DOLPHINS: Dataset for Collaborative Perception enabled Harmonious and Interconnected Self-driving
Ruiqing Mao, Jingyu Guo, Yukuan Jia +3
Vehicle-to-Everything (V2X) network has enabled collaborative perception in autonomous driving, which is a promising solution to the fundamental defect of stand-alone intelligence…
Multi-user Co-inference with Batch Processing Capable Edge Server
Wenqi Shi, Sheng Zhou, Zhisheng Niu +2
Graphics processing units (GPUs) can improve deep neural network inference throughput via batch processing, where multiple tasks are concurrently processed. We focus on novel scena…
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…
Online V2X Scheduling for Raw-Level Cooperative Perception
Yukuan Jia, Ruiqing Mao, Yuxuan Sun +2
Cooperative perception of connected vehicles comes to the rescue when the field of view restricts stand-alone intelligence. While raw-level cooperative perception preserves most in…