1 citations · 1 across the 3 of their papers we have counts for
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cs.NI2023
When Computing Power Network Meets Distributed Machine Learning: An Efficient Federated Split Learning Framework
Xinjing Yuan, Lingjun Pu, Lei Jiao +3
In this paper, we advocate CPN-FedSL, a novel and flexible Federated Split Learning (FedSL) framework over Computing Power Network (CPN). We build a dedicated model to capture the…
cs.NI2020
Cost-efficient and Skew-aware Data Scheduling for Incremental Learning in 5G Network
Lingjun Pu, Xinjing Yuan, Xiaohang Xu +3
To facilitate the emerging applications in 5G networks, mobile network operators will provide many network functions in terms of control and prediction. Recently, they have recogni…
cs.NI2016★ 1 cited
Content Retrieval At the Edge: A Social-aware and Named Data Cooperative Framework
Lingjun Pu, Xu Chen, Jingdong Xu +1
Recent years with the popularity of mobile devices have witnessed an explosive growth of mobile multimedia contents which dominate more than 50\% of mobile data traffic. This signi…