activity
20242026
most citedMobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks

3 citations · 3 across the 4 of their papers we have counts for

collaborators

5 papers

cs.MM2026

GeoFovea-GS: Geometry-Aware Cross-Layer Gaussian Splatting for Wireless Aerial VR

Zeyi Ren, Wencheng Yan, Jiawen Zhang +3

Wireless aerial virtual reality (VR) aims to provide immersive access to large-scale scenes, but high-resolution view generation and delivery are jointly constrained by limited ban…

cs.LG2025

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…

cs.LG2025

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…

cs.LG2024

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…

cs.LG2024★ 3 cited

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…