8 citations · 14 across the 4 of their papers we have counts for
7 papers
Client Selection and Bandwidth Allocation for Federated Learning: An Online Optimization Perspective
Yun Ji, Zhoubin Kou, Xiaoxiong Zhong +3
Federated learning (FL) can train a global model from clients' local data set, which can make full use of the computing resources of clients and performs more extensive and efficie…
Auction Based Clustered Federated Learning in Mobile Edge Computing System
Renhao Lu, Weizhe Zhang, Qiong Li +2
In recent years, mobile clients' computing ability and storage capacity have greatly improved, efficiently dealing with some applications locally. Federated learning is a promising…
CFLMEC: Cooperative Federated Learning for Mobile Edge Computing
Xinghan Wang, Xiaoxiong Zhong, Yuanyuan Yang +1
We investigate a cooperative federated learning framework among devices for mobile edge computing, named CFLMEC, where devices co-exist in a shared spectrum with interference. Keep…
A Parallel Optimal Task Allocation Mechanism for Large-Scale Mobile Edge Computing
Xiaoxiong Zhong, Xinghan Wang, Yuanyuan Yang +3
We consider the problem of intelligent and efficient task allocation mechanism in large-scale mobile edge computing (MEC), which can reduce delay and energy consumption in a parall…
OODT: Obstacle Aware Opportunistic Data Transmission for Cognitive Radio Ad Hoc Networks
Xiaoxiong Zhong, Li Li, Yuanping Zhang +3
In recent years, a large number of smart devices will be connected in Internet of Things (IoT) using an ad hoc network, which needs more frequency spectra. The cognitive radio (CR)…
CL-ADMM: A Cooperative Learning Based Optimization Framework for Resource Management in MEC
Xiaoxiong Zhong, Xinghan Wang, Li Li +5
We consider the problem of intelligent and efficient resource management framework in mobile edge computing (MEC), which can reduce delay and energy consumption, featuring distribu…