22 citations · 48 across the 14 of their papers we have counts for
4 papers · 1 filter
Split Learning in 6G Edge Networks
Zheng Lin, Guanqiao Qu, Xianhao Chen +1
With the proliferation of distributed edge computing resources, the 6G mobile network will evolve into a network for connected intelligence. Along this line, the proposal to incorp…
Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interference
Zhanwei Wang, Kaibin Huang, Yonina C. Eldar
Federated Learning (FL) is a widely embraced paradigm for distilling artificial intelligence from distributed mobile data. However, the deployment of FL in mobile networks can be c…
Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks
Zheng Lin, Guangyu Zhu, Yiqin Deng +4
The increasingly deeper neural networks hinder the democratization of privacy-enhancing distributed learning, such as federated learning (FL), to resource-constrained devices. To o…
Vertical Layering of Quantized Neural Networks for Heterogeneous Inference
Hai Wu, Ruifei He, Haoru Tan +2
Although considerable progress has been obtained in neural network quantization for efficient inference, existing methods are not scalable to heterogeneous devices as one dedicated…