4 papers
ACPSL: Adaptive Communication-Computation Pipeline Parallel Split Learning over Edge Networks
Chenyu Liu, Zhaoyang Zhang, Zirui Chen +3
In wireless edge networks, split learning (SL) enables base station (BS) to utilize the distributed data and computing power across user equipments (UEs) to achieve collaborative m…
Agentic AI-RAN Empowering Synergetic Sensing, Communication, Computing, and Control
Lingxiao Sun, Zhaoyang Zhang, Zihan Lin +4
Future sixth-generation (6G) networks are expected to support low-altitude wireless networks (LAWNs), where unmanned aerial vehicles (UAVs) and aerial robots operate in highly dyna…
Magnitude Matters: Fixing SIGNSGD Through Magnitude-Aware Sparsification in the Presence of Data Heterogeneity
Richeng Jin, Xiaofan He, Caijun Zhong +3
Communication overhead has become one of the major bottlenecks in the distributed training of deep neural networks. To alleviate the concern, various gradient compression methods h…
Breaking the Communication-Privacy-Accuracy Tradeoff with -Differential Privacy
Richeng Jin, Zhonggen Su, Caijun Zhong +3
We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capab…