1 citations · 3 across the 15 of their papers we have counts for
17 papers
Discovering and Preserving Category Correlation Knowledge via Adaptive Reciprocal Knowledge Distillation
Dawen Jiang, Zhishu Shen, Zeyu Liu +1
Knowledge distillation aims to improve the performance of lightweight student models by transferring knowledge from larger and more powerful teacher models. However, a substantial…
Counterfactual Benchmarking and Training for Factuality Consistency and Order-Robust Grounded Reasoning in LLMs over Heterogeneous Knowledge
Shibo Chu, Yuze Liu, Tiehua Zhang +4
Large language models (LLMs) have increasingly supported response generation grounded in user-provided knowledge spanning heterogeneous structures. However, existing benchmarks pro…
A RAG-Enhanced Bi-Level Cognitive Orchestration Framework for LEO Satellite Networks
Yuhong Jiang, Zhishu Shen, Tong Yin +4
The rapid growth of remote sensing data in Low Earth Orbit (LEO) satellite networks is increasingly constrained by limited downlink capacity to terrestrial networks. Satellite edge…
Route What Remains: A Meta-Modal Agent for Missing-Modality Candidate Reranking in Recommender Systems
Jinze Wang, Yangchen Zeng, Tiehua Zhang +5
Missing-modality recommenders usually reconstruct absent representations, although the observed evidence may not determine the missing content. We formulate candidate reranking as…
ML-ECS: A Collaborative Multimodal Learning Framework for Edge-Cloud Synergies
Yuze Liu, Shibo Chu, Tiehua Zhang +5
Edge-cloud synergies provide a promising paradigm for privacy-preserving deployment of foundation models, where lightweight on-device models adapt to domain-specific data and cloud…
A Structure-Agnostic Co-Tuning Framework for LLMs and SLMs in Cloud-Edge Systems
Yuze Liu, Yunhan Wang, Tiehua Zhang +5
The surge in intelligent applications driven by large language models (LLMs) has made it increasingly difficult for bandwidth-limited cloud servers to process extensive LLM workloa…
Do We Really Need SFT? Prompt-as-Policy over Knowledge Graphs for Cold-start Next POI Recommendation
Jinze Wang, Lu Zhang, Yiyang Cui +5
Next point-of-interest (POI) recommendation is a key component of smart urban services, yet it remains challenging under cold-start conditions with sparse user-POI interactions. Re…
Towards Heterogeneity-Aware and Energy-Efficient Topology Optimization for Decentralized Federated Learning in Edge Environment
Yuze Liu, Tiehua Zhang, Zhishu Shen +3
Federated learning (FL) has emerged as a promising paradigm within edge computing (EC) systems, enabling numerous edge devices to collaboratively train artificial intelligence (AI)…
A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks
Zhuocheng Liu, Zhishu Shen, Qiushi Zheng +3
Low Earth Orbit (LEO) satellites are emerging as key components of 6G networks, with many already deployed to support large-scale Earth observation and sensing related tasks. Feder…
Intelligent Task Management via Dynamic Multi-region Division in LEO Satellite Networks
Zixuan Song, Zhishu Shen, Xiaoyu Zheng +3
As a key complement to terrestrial networks and a fundamental component of future 6G systems, Low Earth Orbit (LEO) satellite networks are expected to provide high-quality communic…
Farm-LightSeek: An Edge-centric Multimodal Agricultural IoT Data Analytics Framework with Lightweight LLMs
Dawen Jiang, Zhishu Shen, Qiushi Zheng +3
Amid the challenges posed by global population growth and climate change, traditional agricultural Internet of Things (IoT) systems is currently undergoing a significant digital tr…
CCRSat: A Collaborative Computation Reuse Framework for Satellite Edge Computing Networks
Ye Zhang, Zhishu Shen, Dawen Jiang +3
In satellite computing applications, such as remote sensing, tasks often involve similar or identical input data, leading to the same processing results. Computation reuse is an em…
FedHC: A Hierarchical Clustered Federated Learning Framework for Satellite Networks
Zhuocheng Liu, Zhishu Shen, Pan Zhou +2
With the proliferation of data-driven services, the volume of data that needs to be processed by satellite networks has significantly increased. Federated learning (FL) is well-sui…
GRL-Prompt: Towards Knowledge Graph based Prompt Optimization via Reinforcement Learning
Yuze Liu, Tingjie Liu, Tiehua Zhang +5
Large language models (LLMs) have demonstrated impressive success in a wide range of natural language processing (NLP) tasks due to their extensive general knowledge of the world.…
Collaborative UAVs Multi-task Video Processing Optimization Based on Enhanced Distributed Actor-Critic Networks
Ziqi Rong, Qiushi Zheng, Zhishu Shen +4
With the rapid advancement of the Internet of Things (IoT) and Artificial Intelligence (AI), intelligent information services are being increasingly integrated across various secto…
A Sharded Blockchain-Based Secure Federated Learning Framework for LEO Satellite Networks
Wenbo Wu, Cheng Tan, Kangcheng Yang +3
Low Earth Orbit (LEO) satellite networks are increasingly essential for space-based artificial intelligence (AI) applications. However, as commercial use expands, LEO satellite net…
Energy Efficient Offloading Policies in Multi-Access Edge Computing Systems with Task Handover
Ling Hou, Shi Li, Zhishu Shen +3
The rapid growth of mobile devices and the increasing complexity of tasks have made energy efficiency a critical challenge in Multi-Access Edge Computing (MEC) systems. This paper…