2 citations · 2 across the 13 of their papers we have counts for
16 papers
Mobility Helps Learning: Unsupervised Model Adaptation for Object Recognition via Movement
Yanan Ma, Yihang Tao, Zhengru Fang +3
Movable agents, such as autonomous vehicles and robots, should be able to autonomously adapt their pre-trained object recognition models to new environments without human annotatio…
HO-FL: Hybrid-Order Federated Learning for Heterogeneous Edge Devices
Qiyuan Chen, Xian Wu, Yanan Ma +1
Federated learning (FL) on memory-constrained edge devices faces a dilemma: first-order (FO) optimization (i.e., backpropagation) demands substantial memory, whereas zeroth-order (…
Update the Unseen Only: Minimizing AoI for Collaborative Perception through Online Learning
Yanan Ma, Zhuoyi Zhao, Zhengru Fang +3
While collaborative perception (CP) enhances the safety of autonomous driving, limited bandwidth can cause severe shared data staleness in CP systems. Existing age-of-information (…
Collaborative Air-Ground Sensing, Communication, Computing, Storage, and Intelligence for Low-Altitude Economy
Yiqin Deng, Junhui Gao, Zihan Fang +3
Low-altitude economy (LAE) is transforming low-altitude airspace into a new cyber-physical infrastructure. Although air-ground communications have been widely studied, LAE is funda…
V2VCrafter: Consistent Street-View Image Generation Across Vehicles
Yihang Tao, Yu Guo, Senkang Hu +4
Connected and autonomous driving (CAD) systems leverage vehicle-to-vehicle (V2V) communication for multi-agent collaborative perception, yet remain constrained by scarce annotated…
Birdcast: Interest-aware BEV Multicasting for Infrastructure-assisted Collaborative Perception
Yanan Ma, Zhengru Fang, Yihang Tao +4
Vehicle-to-infrastructure collaborative perception (V2I-CP) leverages a high-vantage node to transmit supplementary information, i.e., bird's-eye-view (BEV) feature maps, to vehicl…
Multi-Server FL with Overlapping Clients: A Latency-Aware Relay Framework
Yun Ji, Zeyu Chen, Xiaoxiong Zhong +3
Multi-server Federated Learning (FL) has emerged as a promising solution to mitigate communication bottlenecks of single-server FL. In a typical multi-server FL architecture, the r…
UAV-enabled Computing Power Networks: Design and Performance Analysis under Energy Constraints
Yiqin Deng, Zhengru Fang, Senkang Hu +4
This paper presents an innovative framework that boosts computing power by utilizing ubiquitous computing power distribution and enabling higher computing node accessibility via ad…
UAV-enabled Computing Power Networks: Task Completion Probability Analysis
Yiqin Deng, Zhengru Fang, Senkang Hu +3
This paper presents an innovative framework that synergistically enhances computing performance through ubiquitous computing power distribution and dynamic computing node accessibi…
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…
FedOC: Multi-Server FL with Overlapping Client Relays in Wireless Edge Networks
Yun Ji, Zeyu Chen, Xiaoxiong Zhong +3
Multi-server Federated Learning (FL) has emerged as a promising solution to mitigate communication bottlenecks of single-server FL. We focus on a typical multi-server FL architectu…
NWaaS: A Non-Intrusive and Privacy-Preserving Watermarking-as-a-Service System with Adaptive Resource Scheduling
Haonan An, Qianyao Ren, Guang Hua +5
Securing intellectual property (IP) in Machine Learning as a Service is critical yet challenging. While deep neural network watermarking serves as a standard defense against model…
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…
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,…