1 citations · 1 across the 9 of their papers we have counts for
9 papers
Delay-Aware Large-Small Model Collaboration over LEO Satellite Networks
Mingyu Guo, Wen Wu, Ying Wang +2
In this paper, we introduce a delay-aware largesmall model collaboration scheme for low Earth orbit (LEO) satellite networks, which can balance the computational load among satelli…
Communication-Efficient Collaborative LLM Inference over LEO Satellite Networks
Songge Zhang, Wen Wu, Liang Li +3
Low Earth orbit (LEO) satellites play an essential role in intelligent Earth observation by leveraging artificial intelligence models. However, limited onboard memory and excessive…
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…
Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions
Zuguang Li, Wen Wu, Shaohua Wu +3
Large models (LMs) have immense potential in Internet of Things (IoT) systems, enabling applications such as intelligent voice assistants, predictive maintenance, and healthcare mo…
Mobility-Aware Federated Learning: Multi-Armed Bandit Based Selection in Vehicular Network
Haoyu Tu, Lin Chen, Zuguang Li +2
In this paper, we study a vehicle selection problem for federated learning (FL) over vehicular networks. Specifically, we design a mobility-aware vehicular federated learning (MAVF…
Split Federated Learning: Speed up Model Training in Resource-Limited Wireless Networks
Songge Zhang, Wen Wu, Penghui Hu +2
In this paper, we propose a novel distributed learning scheme, named group-based split federated learning (GSFL), to speed up artificial intelligence (AI) model training. Specifica…