3 citations · 4 across the 5 of their papers we have counts for
6 papers
FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning
Yuxuan Bai, Yuxuan Sun, Tan Chen +3
Federated edge learning (FEEL) enables collaborative model training across distributed clients over wireless networks without exposing raw data. While most existing studies assume…
FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning
Tan Chen, Jintao Yan, Yuxuan Sun +2
Federated learning (FL) is a promising paradigm for multiple devices to cooperatively train a model. When applied in wireless networks, two issues consistently affect the performan…
Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsification
Jintao Yan, Tan Chen, Yuxuan Sun +3
Asynchronous Federated Learning (AFL) enables distributed model training across multiple mobile devices, allowing each device to independently update its local model without waitin…
Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learning
Jintao Yan, Tan Chen, Yuxuan Sun +3
Leveraging the computing and sensing capabilities of vehicles, vehicular federated learning (VFL) has been applied to edge training for connected vehicles. The dynamic and intercon…
Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks
Tan Chen, Jintao Yan, Yuxuan Sun +3
Hierarchical federated learning (HFL) enables distributed training of models across multiple devices with the help of several edge servers and a cloud edge server in a privacy-pres…
Data-Heterogeneous Hierarchical Federated Learning with Mobility
Tan Chen, Jintao Yan, Yuxuan Sun +3
Federated learning enables distributed training of machine learning (ML) models across multiple devices in a privacy-preserving manner. Hierarchical federated learning (HFL) is fur…