no code implementations • 23 Jun 2022 • Yunwei Tao, Yanxiang Jiang, Fu-Chun Zheng, Pengcheng Zhu, Dusit Niyato, Xiaohu You
To utilize the computing resources of other fog access points (F-APs) and to reduce the communications overhead, we propose a quantized federated learning (FL) framework combining with Bayesian learning.
no code implementations • 23 Jun 2022 • Yanxiang Jiang, Min Zhang, Fu-Chun Zheng, Yan Chen, Mehdi Bennis, Xiaohu You
In this paper, cooperative edge caching problem is studied in fog radio access networks (F-RANs).
no code implementations • 13 Jun 2022 • Zhiheng Wang, Yanxiang Jiang, Fu-Chun Zheng, Mehdi Bennis, Xiaohu You
Based on clustered federated learning, we propose a novel mobility-aware popularity prediction policy, which integrates content popularities in terms of local users and mobile users.
no code implementations • 13 Jun 2022 • Lingling Zhang, Yanxiang Jiang, Fu-Chun Zheng, Mehdi Bennis, Xiaohu You
In this paper, by considering time-varying network environment, a dynamic computation offloading and resource allocation problem in F-RANs is formulated to minimize the task execution delay and energy consumption of MDs.
no code implementations • 27 Feb 2019 • Liuyang Lu, Yanxiang Jiang, Mehdi Bennis, Zhiguo Ding, Fu-Chun Zheng, Xiaohu You
In this paper, the distributed edge caching problem in fog radio access networks (F-RANs) is investigated.