1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2026
Lean Clients, Full Accuracy: Hybrid Zeroth- and First-Order Split Federated Learning
Zhoubin Kou, Zihan Chen, Jing Yang +1
Split Federated Learning (SFL) enables collaborative training between resource-constrained edge devices and a compute-rich server. Communication overhead is a central issue in SFL…
cs.LG2023
Asynchronous Federated Learning with Incentive Mechanism Based on Contract Theory
Danni Yang, Yun Ji, Zhoubin Kou +2
To address the challenges posed by the heterogeneity inherent in federated learning (FL) and to attract high-quality clients, various incentive mechanisms have been employed. Howev…
cs.LG2023★ 1 cited
Semi-Asynchronous Federated Edge Learning Mechanism via Over-the-air Computation
Zhoubin Kou, Yun Ji, Xiaoxiong Zhong +1
Over-the-air Computation (AirComp) has been demonstrated as an effective transmission scheme to boost the efficiency of federated edge learning (FEEL). However, existing FEEL syste…