20 citations · 59 across the 26 of their papers we have counts for
7 papers · 1 filter
Federated Meta-Learning for Few-Shot Fault Diagnosis with Representation Encoding
Jixuan Cui, Jun Li, Zhen Mei +5
Deep learning-based fault diagnosis (FD) approaches require a large amount of training data, which are difficult to obtain since they are located across different entities. Federat…
Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity
Xuefeng Han, Jun Li, Wen Chen +4
With the rapid proliferation of smart mobile devices, federated learning (FL) has been widely considered for application in wireless networks for distributed model training. Howeve…
Deep Reinforcement Learning Based Intelligent Reflecting Surface Optimization for TDD MultiUser MIMO Systems
Fengyu Zhao, Wen Chen, Ziwei Liu +2
In this letter, we investigate the discrete phase shift design of the intelligent reflecting surface (IRS) in a time division duplexing (TDD) multi-user multiple input multiple out…
Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learning
Kecheng Fan, Wen Chen, Jun Li +3
As an efficient distributed machine learning approach, Federated learning (FL) can obtain a shared model by iterative local model training at the user side and global model aggrega…
Collaborative Multi-BS Power Management for Dense Radio Access Network using Deep Reinforcement Learning
Yuchao Chang, Wen Chen, Jun Li +4
Network energy efficiency is a main pillar in the design and operation of wireless communication systems. In this paper, we investigate a dense radio access network (dense-RAN) cap…
Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy
Kang Wei, Jun Li, Chuan Ma +5
Federated learning (FL) enables distributed clients to collaboratively train a machine learning model without sharing raw data with each other. However, it suffers the leakage of p…
Design of Two-Level Incentive Mechanisms for Hierarchical Federated Learning
Shunfeng Chu, Jun Li, Kang Wei +4
Hierarchical Federated Learning (HFL) is a distributed machine learning paradigm tailored for multi-tiered computation architectures, which supports massive access of devices' mode…