1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.NI2025
CoMoE: Collaborative Optimization of Expert Aggregation and Offloading for MoE-based LLMs at Edge
Muqing Li, Ning Li, Xin Yuan +4
The proliferation of large language models (LLMs) has driven the adoption of Mixture-of-Experts (MoE) architectures as a promising solution to scale model capacity while controllin…
cs.NI2025★ 1 cited
The MoE-Empowered Edge LLMs Deployment: Architecture, Challenges, and Opportunities
Ning Li, Song Guo, Tuo Zhang +5
The powerfulness of LLMs indicates that deploying various LLMs with different scales and architectures on end, edge, and cloud to satisfy different requirements and adaptive hetero…
cs.DC2023
High Efficiency Inference Accelerating Algorithm for NOMA-based Mobile Edge Computing
Xin Yuan, Ning Li, Tuo Zhang +4
Splitting the inference model between device, edge server, and cloud can improve the performance of EI greatly. Additionally, the non-orthogonal multiple access (NOMA), which is th…