43 citations · 138 across the 29 of their papers we have counts for
32 papers
Sylvas: Synergistic Learning Value based Device Scheduling in Federated Continual Learning
Yuxuan Sun, Yuxuan Bai, Tan Chen +2
Federated continual learning (FCL) enables shared global models to continuously adapt to distributed and non-stationary data streams, making it important for Internet of Things app…
FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning
Yuxuan Bai, Yuxuan Sun, Tan Chen +3
Federated edge learning (FEEL) enables collaborative model training across distributed clients over wireless networks without exposing raw data. While most existing studies assume…
FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning
Tan Chen, Jintao Yan, Yuxuan Sun +2
Federated learning (FL) is a promising paradigm for multiple devices to cooperatively train a model. When applied in wireless networks, two issues consistently affect the performan…
Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsification
Jintao Yan, Tan Chen, Yuxuan Sun +3
Asynchronous Federated Learning (AFL) enables distributed model training across multiple mobile devices, allowing each device to independently update its local model without waitin…
DiffCP: Ultra-Low Bit Collaborative Perception via Diffusion Model
Ruiqing Mao, Haotian Wu, Yukuan Jia +5
Collaborative perception (CP) is emerging as a promising solution to the inherent limitations of stand-alone intelligence. However, current wireless communication systems are unabl…
C-MASS: Combinatorial Mobility-Aware Sensor Scheduling for Collaborative Perception with Second-Order Topology Approximation
Yukuan Jia, Yuxuan Sun, Ruiqing Mao +3
Collaborative Perception (CP) has been a promising solution to address occlusions in the traffic environment by sharing sensor data among collaborative vehicles (CoV) via vehicle-t…
Task-Oriented Wireless Communications for Collaborative Perception in Intelligent Unmanned Systems
Sheng Zhou, Yukuan Jia, Ruiqing Mao +3
Collaborative Perception (CP) has shown great potential to achieve more holistic and reliable environmental perception in intelligent unmanned systems (IUSs). However, implementing…
Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learning
Jintao Yan, Tan Chen, Yuxuan Sun +3
Leveraging the computing and sensing capabilities of vehicles, vehicular federated learning (VFL) has been applied to edge training for connected vehicles. The dynamic and intercon…
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…
Digital-SC: Digital Semantic Communication with Adaptive Network Split and Learned Non-Linear Quantization
Lei Guo, Wei Chen, Yuxuan Sun +1
Semantic communication, an intelligent communication paradigm that aims to transmit useful information in the semantic domain, is facilitated by deep learning techniques. Robust se…
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…
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…
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…
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…