1 citations · 1 across the 9 of their papers we have counts for
4 papers · 1 filter
Fast AI Model Partition for Split Learning over Edge Networks
Zuguang Li, Wen Wu, Shaohua Wu +2
Split learning (SL) is a distributed learning paradigm that can enable computation-intensive artificial intelligence (AI) applications by partitioning AI models between mobile devi…
LLM-Empowered IoT for 6G Networks: Architecture, Challenges, and Solutions
Xiaopei Chen, Wen Wu, Liang Li +1
The Internet of Things (IoT) in the sixth generation (6G) era is envisioned to evolve towards intelligence, ubiquity, and self-optimization. Large language models (LLMs) have demon…
SplitLLM: Hierarchical Split Learning for Large Language Model over Wireless Network
Songge Zhang, Guoliang Cheng, Zuguang Li +1
Fine-tuning a large language model (LLM) using the local data of edge users can enable personalized services and applications. For privacy protection, the prevalent solution adopts…
Split Fine-Tuning for Large Language Models in Wireless Networks
Songge Zhang, Guoliang Cheng, Xinyu Huang +4
Fine-tuning is the process of adapting the pre-trained large language models (LLMs) for downstream tasks. Due to substantial parameters, fine-tuning LLMs on mobile devices demands…