4 citations · 7 across the 9 of their papers we have counts for
10 papers
CFLoRA: Federated Fine-tuning of LLMs with Complementary Factors for Error-free Aggregation
Yanan Ma, Qiyuan Chen, Zihan Fang +2
Federated low-rank adaptation (LoRA) enables collaborative fine-tuning of large language models without centralizing private client data. Its factorized update, however, creates a…
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.…
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…
CA3D: Computing Accessibility-Aware Cooperative 3D Deployment of Multiple UAVs
Yiqin Deng, Zihan Fang, Yijie Wang +4
This letter investigates computing-accessibility-aware cooperative 3D deployment of multiple UAVs for task completion enhancement, termed CA3D. We first provide a theoretical analy…
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…
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…
SAFE-D: A Spatiotemporal Detection Framework for Abnormal Driving Among Parkinson's Disease-like Drivers
Hangcheng Cao, Baixiang Huang, Longzhi Yuan +4
A driver's health state serves as a determinant factor in driving behavioral regulation. Subtle deviations from normalcy can lead to operational anomalies, posing risks to public t…
Distribution-Aligned Decoding for Efficient LLM Task Adaptation
Senkang Hu, Xudong Han, Jinqi Jiang +5
Adapting billion-parameter language models to a downstream task is still costly, even with parameter-efficient fine-tuning (PEFT). We re-cast task adaptation as output-distribution…
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…