7 papers
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…
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…
Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks
Zuguang Li, Shaohua Wu, Liang Li +1
In this letter, we propose an energy-efficient split learning (SL) framework for fine-tuning large language models (LLMs) using geo-distributed personal data at the network edge, w…
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…