20 citations · 21 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
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…