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cs.LG2025
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…
cs.LG2024
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…
cs.LG2024
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…