20 citations · 21 across the 8 of their papers we have counts for
9 papers
Low-Latency Federated Fine-Tuning for Large Language Models Over Wireless Networks
Zhiwen Pang, Kang Wei, Long Shi +3
Recently, federated large language models (LLMs) have drawn significant attention thanks to coupled capabilities of LLMs and federated learning (FL) that address privacy concerns i…
Stable-MoE: Lyapunov-based Token Routing for Distributed Mixture-of-Experts Training over Edge Networks
Long Shi, Bingyan Ou, Kang Wei +3
The sparse activation mechanism of mixture of experts (MoE) model empowers edge intelligence with enhanced training efficiency and reduced computational resource consumption. Howev…
When MoE Meets Blockchain: A Trustworthy Distributed Framework of Large Models
Weihao Zhu, Long Shi, Kang Wei +4
As an enabling architecture of Large Models (LMs), Mixture of Experts (MoE) has become prevalent thanks to its sparsely-gated mechanism, which lowers computational overhead while m…
Decision Transformers for RIS-Assisted Systems with Diffusion Model-Based Channel Acquisition
Jie Zhang, Yiyang Ni, Jun Li +6
Reconfigurable intelligent surfaces (RISs) have been recognized as a revolutionary technology for future wireless networks. However, RIS-assisted communications have to continuousl…
Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach
Fei Song, Zhe Wang, Jun Li +3
In ultra-dense unmanned aerial vehicle (UAV) networks, it is challenging to coordinate the resource allocation and interference management among large-scale UAVs, for providing fle…
Decision Transformer for IRS-Assisted Systems with Diffusion-Driven Generative Channels
Jie Zhang, Jun Li, Zhe Wang +3
In this paper, we propose a novel diffusion-decision transformer (D2T) architecture to optimize the beamforming strategies for intelligent reflecting surface (IRS)-assisted multipl…
Decision Transformers for Wireless Communications: A New Paradigm of Resource Management
Jie Zhang, Jun Li, Long Shi +4
As the next generation of mobile systems evolves, artificial intelligence (AI) is expected to deeply integrate with wireless communications for resource management in variable envi…
Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control
Ziyan Yin, Zhe Wang, Jun Li +3
The explosive growth of dynamic and heterogeneous data traffic brings great challenges for 5G and beyond mobile networks. To enhance the network capacity and reliability, we propos…
Federated Learning Over Wireless Channels: Dynamic Resource Allocation and Task Scheduling
Shunfeng Chu, Jun Li, Jianxin Wang +5
With the development of federated learning (FL), mobile devices (MDs) are able to train their local models with private data and sends them to a central server for aggregation, the…