8 citations · 28 across the 29 of their papers we have counts for
34 papers
PSL-RGHA: Parallel Split Learning with Reference Gradient Half-Space Alignment for Mitigating Gradient Divergence
Han Li, Zheng Lin, Miao Yang +9
The escalating complexity of deep neural networks hinders the deployment of federated learning (FL) on resource-constrained devices, especially for multimedia applications such as…
Redundancy Meets Synergy: Dependency-aware Expert Selection for MoE via Submodular Optimization
Zheng Lin, Shaoke Fang, Yuxin Zhang +6
While Mixture-of-Experts (MoE) models effectively scale model capacity through sparse activation, their deployment is often bottlenecked by prohibitive memory requirements. Extract…
Conflict-Aware Federated Fine-Tuning of Large Language Models with Mixture-of-Experts
Yijun Lu, Zihan Fang, Pengpeng Qiao +6
The continuous scaling of large language models (LLMs) incurs prohibitive computational costs, making Mixture-of-Experts (MoE) a scalable alternative for efficient fine-tuning via…
Hera: Learning Long-Horizon Coordination for Device-Cloud Collaborative LLM Agents
Yuxin Zhang, Mengxue Hu, Zheng Lin +8
Large language model (LLM) agents excel at solving complex long-horizon tasks through autonomous interaction with environments. However, their real-world deployment faces a fundame…
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…
Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classification
Weiwei Zhuang, Wangze Xie, Qi Zhang +9
Adversarial attacks pose a severe threat to the reliability of deep learning models in remote sensing (RS) image classification. Most existing methods rely on direct pixel-wise per…
SL-FAC: A Communication-Efficient Split Learning Framework with Frequency-Aware Compression
Zehang Lin, Miao Yang, Haihan Zhu +9
The growing complexity of neural networks hinders the deployment of distributed machine learning on resource-constrained devices. Split learning (SL) offers a promising solution by…
Exploiting Adaptive Channel Pruning for Communication-Efficient Split Learning
Jialei Tan, Zheng Lin, Xiangming Cai +4
Split learning (SL) transfers most of the training workload to the server, which alleviates computational burden on client devices. However, the transmission of intermediate featur…
Transformer-Based Multipath Congestion Control: A Decoupled Approach for Wireless Uplinks
Zongyuan Zhang, Tianyang Duan, Liang Wang +9
The proliferation of artificial intelligence applications on edge devices necessitates efficient transport protocols that leverage multi-homed connectivity across heterogeneous net…
GAPSL: A Gradient-Aligned Parallel Split Learning over Data-Heterogeneous Edge Computing Systems
Zheng Lin, Ons Aouedi, Zihan Fang +4
The increasing complexity of neural networks poses significant challenges for democratizing federated learning (FL) on resource-constrained edge devices. Parallel split learning (P…
SIDeR: Semantic Identity Decoupling for Unrestricted Face Privacy
Zhuosen Bao, Xia Du, Zheng Lin +8
With the deep integration of facial recognition into online banking, identity verification, and other networked services, achieving effective decoupling of identity information fro…
NSC-SL: A Bandwidth-Aware Neural Subspace Compression for Communication-Efficient Split Learning
Zhen Fang, Miao Yang, Zehang Lin +6
The expanding scale of neural networks poses a major challenge for distributed machine learning, particularly under limited communication resources. While split learning (SL) allev…
Conflict-Aware Client Selection for Multi-Server Federated Learning
Mingwei Hong, Zheng Lin, Zehang Lin +7
Federated learning (FL) has emerged as a promising distributed machine learning (ML) that enables collaborative model training across clients without exposing raw data, thereby pre…
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…
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…
Sample Efficient Experience Replay in Non-stationary Environments
Tianyang Duan, Zongyuan Zhang, Songxiao Guo +8
Reinforcement learning (RL) in non-stationary environments is challenging, as changing dynamics and rewards quickly make past experiences outdated. Traditional experience replay (E…
LEED: A Highly Efficient and Scalable LLM-Empowered Expert Demonstrations Framework for Multi-Agent Reinforcement Learning
Tianyang Duan, Zongyuan Zhang, Songxiao Guo +7
Multi-agent reinforcement learning (MARL) holds substantial promise for intelligent decision-making in complex environments. However, it suffers from a coordination and scalability…
SL-ACC: A Communication-Efficient Split Learning Framework with Adaptive Channel-wise Compression
Zehang Lin, Zheng Lin, Miao Yang +7
The increasing complexity of neural networks poses a significant barrier to the deployment of distributed machine learning (ML) on resource-constrained devices, such as federated l…
Dynamic Uncertainty-aware Multimodal Fusion for Outdoor Health Monitoring
Zihan Fang, Zheng Lin, Senkang Hu +4
Outdoor health monitoring is essential to detect early abnormal health status for safeguarding human health and safety. Conventional outdoor monitoring relies on static multimodal…
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…
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…
HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models
Zheng Lin, Yuxin Zhang, Zhe Chen +6
Recently, large language models (LLMs) have achieved remarkable breakthroughs, revolutionizing the natural language processing domain and beyond. Due to immense parameter sizes, fi…
Robust Deep Reinforcement Learning in Robotics via Adaptive Gradient-Masked Adversarial Attacks
Zongyuan Zhang, Tianyang Duan, Zheng Lin +8
Deep reinforcement learning (DRL) has emerged as a promising approach for robotic control, but its realworld deployment remains challenging due to its vulnerability to environmenta…
State-Aware Perturbation Optimization for Robust Deep Reinforcement Learning
Zongyuan Zhang, Tianyang Duan, Zheng Lin +7
Recently, deep reinforcement learning (DRL) has emerged as a promising approach for robotic control. However, the deployment of DRL in real-world robots is hindered by its sensitiv…