190 citations · 276 across the 11 of their papers we have counts for
4 papers · 1 filter
Asynchronous Federated Learning Based Mobility-aware Caching in Vehicular Edge Computing
Wenhua Wang, Yu Zhao, Qiong Wu +3
Vehicular edge computing (VEC) is a promising technology to support real-time applications through caching the contents in the roadside units (RSUs), thus vehicles can fetch the co…
Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning
Qiong Wu, Yu Zhao, Qiang Fan +3
The vehicular edge computing (VEC) can cache contents in different RSUs at the network edge to support the real-time vehicular applications. In VEC, owing to the high-mobility char…
High stable and accurate vehicle selection scheme based on federated edge learning in vehicular networks
Qiong Wu, Xiaobo Wang, Qiang Fan +3
Federated edge learning (FEEL) technology for vehicular networks is considered as a promising technology to reduce the computation workload while keeping the privacy of users. In t…
Towards V2I Age-aware Fairness Access: A DQN Based Intelligent Vehicular Node Training and Test Method
Qiong Wu, Shuai Shi, Ziyang Wan +3
Vehicles on the road exchange data with base station (BS) frequently through vehicle to infrastructure (V2I) communications to ensure the normal use of vehicular applications, wher…