6 papers
Initiating Removal from Operational Symptoms in Federated Vehicular Networks
Zihao Ding, Jun Huang, Qiang Duan
Connected vehicles train a driving model through federated learning (FL) and drive with it, so a faulty contribution often surfaces only as a delayed symptom on the road. Federated…
Splitting Prompt Prefill from Response Replay for Context-Parallel Long-Context LLM Post-Training
Yubing Bao, Zhihui Lu, Qiang Duan +3
Training long-context LLM policies with RL requires re-evaluating groups of sampled responses under the updated policy, an update-stage attention workload that differs sharply from…
FedSceneX: Time-to-Target Orchestration for Same-Scene Multimodal Federated Edge Learning
Dhe Yeong Tchalla, Beining Wu, Jun Huang +2
Federated learning at the sensing edge is typically evaluated by communication rounds, yet a round does not represent a fixed amount of work. Even on identical hardware, the method…
MOC: Multi-Order Communication in LLM-based Multi-Agent Systems
Yao Guan, Lin Wang, Zhihu Lu +3
Despite the remarkable progress of Large Language Model (LLM) based Multi-Agent Systems, most research focuses on optimizing coordination topology while largely underexploring the…
Enhancing Vehicular Platooning with Wireless Federated Learning: A Resource-Aware Control Framework
Beining Wu, Jun Huang, Qiang Duan +2
This paper aims to enhance the performance of Vehicular Platooning (VP) systems integrated with Wireless Federated Learning (WFL). In highly dynamic environments, vehicular platoon…
A Low-latency Communication Design for Brain Simulations
Xin Du
Brain simulation, as one of the latest advances in artificial intelligence, facilitates better understanding about how information is represented and processed in the brain. The ex…