2 citations · 3 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Client Selection and Bandwidth Allocation for Federated Learning: An Online Optimization Perspective
Yun Ji, Zhoubin Kou, Xiaoxiong Zhong +3
Federated learning (FL) can train a global model from clients' local data set, which can make full use of the computing resources of clients and performs more extensive and efficie…
Long-Term CSI-based Design for RIS-Aided Multiuser MISO Systems Exploiting Deep Reinforcement Learning
Hong Ren, Cunhua Pan, Liang Wang +2
In this paper, we study the transmission design for reconfigurable intelligent surface (RIS)-aided multiuser communication networks. Different from most of the existing contributio…