31 citations · 60 across the 13 of their papers we have counts for
5 papers · 1 filter
Optimizing Split Federated Learning with Unstable Client Participation
Wei Wei, Zheng Lin, Xihui Liu +3
To enable training of large artificial intelligence (AI) models at the network edge, split federated learning (SFL) has emerged as a promising approach by distributing computation…
Pipelining Split Learning in Multi-hop Edge Networks
Wei Wei, Zheng Lin, Tao Li +2
To support large-scale model training, split learning (SL) enables multiple edge devices/servers to share the intensive training workload. However, most existing works on SL focus…
Mobile Edge Intelligence for Large Language Models: A Contemporary Survey
Guanqiao Qu, Qiyuan Chen, Wei Wei +3
On-device large language models (LLMs), referring to running LLMs on edge devices, have raised considerable interest since they are more cost-effective, latency-efficient, and priv…
TrimCaching: Parameter-sharing AI Model Caching in Wireless Edge Networks
Guanqiao Qu, Zheng Lin, Fangming Liu +2
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…
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…