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cs.DC2025
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…
cs.DC2025
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…
eess.SP2025
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…