172 citations · 1.4k across the 92 of their papers we have counts for
93 papers
Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks
Tan Chen, Jintao Yan, Yuxuan Sun +3
Hierarchical federated learning (HFL) enables distributed training of models across multiple devices with the help of several edge servers and a cloud edge server in a privacy-pres…
Optimizing Task-Specific Timeliness With Edge-Assisted Scheduling for Status Update
Jingzhou Sun, Lehan Wang, Zhaojun Nan +3
Intelligent real-time applications, such as video surveillance, demand intensive computation to extract status information from raw sensing data. This poses a substantial challenge…
A Grouping-based Scheduler for Efficient Channel Utilization under Age of Information Constraints
Lehan Wang, Jingzhou Sun, Yuxuan Sun +2
We consider a status information updating system where a fusion center collects the status information from a large number of sources and each of them has its own age of informatio…
Age of Information Guaranteed Scheduling for Asynchronous Status Updates in Collaborative Perception
Lehan Wang, Jingzhou Sun, Yuxuan Sun +2
We consider collaborative perception (CP) systems where a fusion center monitors various regions by multiple sources. The center has different age of information (AoI) constraints…
Data-Heterogeneous Hierarchical Federated Learning with Mobility
Tan Chen, Jintao Yan, Yuxuan Sun +3
Federated learning enables distributed training of machine learning (ML) models across multiple devices in a privacy-preserving manner. Hierarchical federated learning (HFL) is fur…
Enhanced Sliding Window Superposition Coding for Industrial Automation
Bohang Zhang, Zhaoujun Nan, Sheng Zhou +1
The introduction of 5G has changed the wireless communication industry. Whereas previous generations of cellular technology are mainly based on communication for people, the wirele…
MASS: Mobility-Aware Sensor Scheduling of Cooperative Perception for Connected Automated Driving
Yukuan Jia, Ruiqing Mao, Yuxuan Sun +2
Timely and reliable environment perception is fundamental to safe and efficient automated driving. However, the perception of standalone intelligence inevitably suffers from occlus…
SMDP-Based Dynamic Batching for Efficient Inference on GPU-Based Platforms
Yaodan Xu, Jingzhou Sun, Sheng Zhou +1
In up-to-date machine learning (ML) applications on cloud or edge computing platforms, batching is an important technique for providing efficient and economical services at scale.…
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…
Coded Computation across Shared Heterogeneous Workers with Communication Delay
Yuxuan Sun, Fan Zhang, Junlin Zhao +3
Distributed computing enables large-scale computation tasks to be processed over multiple workers in parallel. However, the randomness of communication and computation delays acros…
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…
Cluster-Based Cooperative Digital Over-the-Air Aggregation for Wireless Federated Edge Learning
Ruichen Jiang, Sheng Zhou
In this paper, we study a federated learning system at the wireless edge that uses over-the-air computation (AirComp). In such a system, users transmit their messages over a multi-…
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…
Dynamic Compression Ratio Selection for Edge Inference Systems with Hard Deadlines
Xiufeng Huang, Sheng Zhou
Implementing machine learning algorithms on Internet of things (IoT) devices has become essential for emerging applications, such as autonomous driving, environment monitoring. But…
Distributed Task Replication for Vehicular Edge Computing: Performance Analysis and Learning-based Algorithm
Yuxuan Sun, Sheng Zhou, Zhisheng Niu
In a vehicular edge computing (VEC) system, vehicles can share their surplus computation resources to provide cloud computing services. The highly dynamic environment of the vehicu…
Urgency of Information for Context-Aware Timely Status Updates in Remote Control Systems
Xi Zheng, Sheng Zhou, Zhisheng Niu
As 5G and Internet-of-Things (IoT) are deeply integrated into vertical industries such as autonomous driving and industrial robotics, timely status update is crucial for remote mon…
AoI-Delay Tradeoff in Mobile Edge Caching with Freshness-Aware Content Refreshing
Shan Zhang, Liudi Wang, Hongbin Luo +2
Mobile edge caching can effectively reduce service delay but may introduce information staleness, calling for timely content refreshing. However, content refreshing consumes additi…
Beyond Age: Urgency of Information for Timeliness Guarantee in Status Update Systems
Xi Zheng, Sheng Zhou, Zhisheng Niu
Timely status updating is crucial for future applications that involve remote monitoring and control, such as autonomous driving and Industrial Internet of Things (IIoT). Age of In…