2 citations · 2 across the 4 of their papers we have counts for
10 papers
SpaceMoE: Realizing Distributed Mixture-of-Experts Inference over Space Networks
Zhanwei Wang, Huiling Yang, Min Sheng +2
Leveraging continuous solar energy harvesting at high efficiency, space data centers are envisioned as a promising platform for executing energy-intensive large language models (LL…
TrimCaching: Parameter-sharing Edge Caching for AI Model Downloading
Guanqiao Qu, Zheng Lin, Qian Chen +4
Next-generation mobile networks are expected to facilitate fast AI model downloading to end users. By caching models on edge servers, mobile networks can deliver models to end user…
SpaceMoE: Towards Orbital General Intelligence with Distributed Mixture-of-Experts Inference
Qian Chen, Xianhao Chen, Min Sheng +1
As satellite networks evolve to support increasingly diverse services and artificial general intelligence (AGI), large language models (LLMs) are emerging as a critical foundation…
SlimCaching: Edge Caching of Mixture-of-Experts for Distributed Inference
Qian Chen, Xianhao Chen, Kaibin Huang
Mixture-of-Experts (MoE) models improve the scalability of large language models (LLMs) by activating only a small subset of relevant experts per input. However, the sheer number o…
FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networks
Qian Chen, Xianhao Chen, Kaibin Huang
To bridge the digital divide, space-ground integrated networks (SGINs) are expected to deliver artificial intelligence (AI) services to every corner of the world. One key mission o…
PartialLoading: User Scheduling and Bandwidth Allocation for Parameter-sharing Edge Inference
Guanqiao Qu, Qian Chen, Xianhao Chen +2
By provisioning inference offloading services, edge inference drives the rapid growth of AI applications at network edge. However, how to reduce the inference latency remains a sig…