47 citations · 183 across the 44 of their papers we have counts for
5 papers · 1 filter
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…
SMDP-Based Dynamic Batching for Efficient Inference on GPU-Based Platforms
Yaodan Xu, Jingzhou Sun, Sheng Zhou +1
In up-to-date machine learning (ML) applications on cloud or edge computing platforms, batching is an important technique for providing efficient and economical services at scale.…
MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehicles
Bowen Xie, Yuxuan Sun, Sheng Zhou +4
Federated learning (FL) is a promising approach to enable the future Internet of vehicles consisting of intelligent connected vehicles (ICVs) with powerful sensing, computing and c…
Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraints
Yuxuan Sun, Sheng Zhou, Zhisheng Niu +1
Machine learning and wireless communication technologies are jointly facilitating an intelligent edge, where federated edge learning (FEEL) is a promising training framework. As wi…