activity
20242026
collaborators

9 papers

cs.NI2026

FluxShard: Motion-Aware Feature Cache Reuse for Collaborative Video Analytics in Mobile Edge Computing

Xiuxian Guan, Zongyuan Zhang, Zheng Lin +8

Caching and reusing intermediate features across consecutive frames is a common technique to reduce redundant computation and transmission for edge-cloud video analytics in mobile…

cs.LG2025

LLM-Driven Stationarity-Aware Expert Demonstrations for Multi-Agent Reinforcement Learning in Mobile Systems

Tianyang Duan, Zongyuan Zhang, Zheng Lin +10

Multi-agent reinforcement learning (MARL) has been increasingly adopted in many real-world applications. While MARL enables decentralized deployment on resource-constrained edge de…

cs.NI2025

RRTO: A High-Performance Transparent Offloading System for Model Inference in Mobile Edge Computing

Zekai Sun, Xiuxian Guan, Zheng Lin +8

Deploying Machine Learning (ML) applications on resource-constrained mobile devices remains challenging due to limited computational resources and poor platform compatibility. Whil…

cs.NI2025

Intra-DP: A High Performance Collaborative Inference System for Mobile Edge Computing

Zekai Sun, Xiuxian Guan, Zheng Lin +8

Deploying deep neural networks (DNNs) on resource-constrained mobile devices presents significant challenges, particularly in achieving real-time performance while simultaneously c…

cs.DC2025

Themis: Efficient Sparse Model Training Through Fully Sharded Sparse Data Parallelism

Yuhao Qing, Guichao Zhu, Lintian Lei +9

Mixture-of-Experts (MoE) scales large language models cost-effectively, but expert-parallel training suffers severe straggler effects from skewed expert loads. Current systems freq…

cs.RO2024

HE-Nav: A High-Performance and Efficient Navigation System for Aerial-Ground Robots in Cluttered Environments

Junming Wang, Zekai Sun, Xiuxian Guan +6

Existing AGR navigation systems have advanced in lightly occluded scenarios (e.g., buildings) by employing 3D semantic scene completion networks for voxel occupancy prediction and…