41 citations · 89 across the 25 of their papers we have counts for
28 papers
More Interactions Make Communications More Efficient: A Directed Information Bottleneck Approach to Task-Oriented Communication
Zhirun Zheng, Yiqin Deng, Phone Lin +1
Task-oriented communication improves communication efficiency by transmitting semantic information required for downstream tasks, but may incur redundancy with previously transmitt…
Multi-Agent Embodied Autonomous Driving (MAEAD): From V2X Information Exchange to Shared World Models
Senkang Hu, Zhengru Fang, Yihang Tao +3
Autonomous driving is shifting from isolated vehicle intelligence toward multi-agent embodied systems that share perception, infer intent, and coordinate action under uncertainty.…
Federated Mixture-of-Experts Alignment on Mobile Edge Networks under Data Heterogeneity
Zihan Fang, Qianru Wang, Haonan An +5
The growing demand for on-device large language model (LLM) services on mobile edge devices has driven the adoption of Mixture-of-Experts (MoE) architectures, which scale model cap…
MLFCIL: A Multi-Level Forgetting Mitigation Framework for Federated Class-Incremental Learning in LEO Satellites
Heng Zhang, Xiaohong Deng, Sijing Duan +4
Low-Earth-orbit (LEO) satellite constellations are increasingly performing on-board computing. However, the continuous emergence of new classes under strict memory and communicatio…
HFedMoE: Resource-aware Heterogeneous Federated Learning with Mixture-of-Experts
Zihan Fang, Zheng Lin, Senkang Hu +5
While federated learning (FL) enables fine-tuning of large language models (LLMs) without compromising data privacy, the substantial size of an LLM renders on-device training impra…
Minimizing Maximum Latency of Task Offloading for Multi-UAV-assisted Maritime Search and Rescue
Shuang Qi, Bin Lin, Yiqin Deng +2
Unmanned Aerial Vehicles (UAVs) play a crucial role in Maritime Search and Rescue (MSAR), contributing to the improvement of rescue efficiency and reduction of casualties. Typicall…
Dynamic Uncertainty-aware Multimodal Fusion for Outdoor Health Monitoring
Zihan Fang, Zheng Lin, Senkang Hu +4
Outdoor health monitoring is essential to detect early abnormal health status for safeguarding human health and safety. Conventional outdoor monitoring relies on static multimodal…
CP-uniGuard: A Unified, Probability-Agnostic, and Adaptive Framework for Malicious Agent Detection and Defense in Multi-Agent Embodied Perception Systems
Senkang Hu, Yihang Tao, Guowen Xu +5
Collaborative Perception (CP) has been shown to be a promising technique for multi-agent autonomous driving and multi-agent robotic systems, where multiple agents share their perce…
Task-Oriented Semantic Compression for Localization at the Network Edge
Zhengru Fang, Senkang Hu, Yu Guo +2
Achieving precise visual localization in GPS-limited urban environments poses significant challenges for resource-constrained mobile platforms, particularly under strict bandwidth,…
Task-Aware Parameter-Efficient Fine-Tuning of Large Pre-Trained Models at the Edge
Senkang Hu, Yanan Ma, Yihang Tao +5
Large language models (LLMs) have achieved remarkable success in various tasks, such as decision-making, reasoning, and question answering. They have been widely used in edge devic…
RAISE: Optimizing RIS Placement to Maximize Task Throughput in Multi-Server Vehicular Edge Computing
Yanan Ma, Zhengru Fang, Longzhi Yuan +3
Given the limited computing capabilities on autonomous vehicles, onboard processing of large volumes of latency-sensitive tasks presents significant challenges. While vehicular edg…
Sense4FL: Vehicular Crowdsensing Enhanced Federated Learning for Object Detection in Autonomous Driving
Yanan Ma, Senkang Hu, Zhengru Fang +3
To accommodate constantly changing road conditions, real-time vision model training is essential for autonomous driving (AD). Federated learning (FL) serves as a promising paradigm…
CP-Guard+: A New Paradigm for Malicious Agent Detection and Defense in Collaborative Perception
Senkang Hu, Yihang Tao, Zihan Fang +4
Collaborative perception (CP) is a promising method for safe connected and autonomous driving, which enables multiple vehicles to share sensing information to enhance perception pe…
CP-Guard: Malicious Agent Detection and Defense in Collaborative Bird's Eye View Perception
Senkang Hu, Yihang Tao, Guowen Xu +4
Collaborative Perception (CP) has shown a promising technique for autonomous driving, where multiple connected and autonomous vehicles (CAVs) share their perception information to…
IC3M: In-Car Multimodal Multi-object Monitoring for Abnormal Status of Both Driver and Passengers
Zihan Fang, Zheng Lin, Senkang Hu +4
Recently, in-car monitoring has emerged as a promising technology for detecting early-stage abnormal status of the driver and providing timely alerts to prevent traffic accidents.…
R-ACP: Real-Time Adaptive Collaborative Perception Leveraging Robust Task-Oriented Communications
Zhengru Fang, Jingjing Wang, Yanan Ma +4
Collaborative perception enhances sensing in multirobot and vehicular networks by fusing information from multiple agents, improving perception accuracy and sensing range. However,…
Resources on the Move for Smart City: A Disruptive Perspective on the Grand Convergence of Sensing, Communications, Computing, Storage, and Intelligence
Yuguang Fang, Yiqin Deng, Xianhao Chen
The most commonly seen things on streets in any city are vehicles. However, most of them are used to transport people or goods. What if they also carry resources and capabilities f…
Prioritized Information Bottleneck Theoretic Framework with Distributed Online Learning for Edge Video Analytics
Zhengru Fang, Senkang Hu, Jingjing Wang +3
Collaborative perception systems leverage multiple edge devices, such surveillance cameras or autonomous cars, to enhance sensing quality and eliminate blind spots. Despite their a…
PIB: Prioritized Information Bottleneck Framework for Collaborative Edge Video Analytics
Zhengru Fang, Senkang Hu, Liyan Yang +3
Collaborative edge sensing systems, particularly in collaborative perception systems in autonomous driving, can significantly enhance tracking accuracy and reduce blind spots with…
AgentsCoMerge: Large Language Model Empowered Collaborative Decision Making for Ramp Merging
Senkang Hu, Zhengru Fang, Zihan Fang +4
Ramp merging is one of the bottlenecks in traffic systems, which commonly cause traffic congestion, accidents, and severe carbon emissions. In order to address this essential issue…
AgentsCoDriver: Large Language Model Empowered Collaborative Driving with Lifelong Learning
Senkang Hu, Zhengru Fang, Zihan Fang +3
Connected and autonomous driving is developing rapidly in recent years. However, current autonomous driving systems, which are primarily based on data-driven approaches, exhibit de…
Collaborative Perception for Connected and Autonomous Driving: Challenges, Possible Solutions and Opportunities
Senkang Hu, Zhengru Fang, Yiqin Deng +2
Autonomous driving has attracted significant attention from both academia and industries, which is expected to offer a safer and more efficient driving system. However, current aut…
Privacy-Preserving Task-Oriented Semantic Communications Against Model Inversion Attacks
Yanhu Wang, Shuaishuai Guo, Yiqin Deng +2
Semantic communication has been identified as a core technology for the sixth generation (6G) of wireless networks. Recently, task-oriented semantic communications have been propos…
Spectrum-aware Multi-hop Task Routing in Vehicle-assisted Collaborative Edge Computing
Yiqin Deng, Haixia Zhang, Xianhao Chen +1
Multi-access edge computing (MEC) is a promising technology to enhance the quality of service, particularly for low-latency services, by enabling computing offloading to edge serve…