3 papers
cs.LG2024
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…
cs.LG2024
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…
cs.LG2024
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…