6 papers
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…
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…
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 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…