no code implementations • 13 Dec 2023 • Ruonan Dong, Hui Xu, Han Zhang, Guopeng Zhang
We formulate the interaction between MO and DOs as an optimization problem, and the objective is to effectively utilize the communication and computing resource of the MO and DOs to minimize the time to complete an FL task.
no code implementations • 26 Aug 2023 • Han Zhang, Halvin Yang, Guopeng Zhang
In vanilla federated learning (FL) such as FedAvg, the parameter server (PS) and multiple distributed clients can form a typical buyer's market, where the number of PS/buyers of FL services is far less than the number of clients/sellers.
no code implementations • 21 Jul 2023 • Yao Wen, Guopeng Zhang, Kezhi Wang, Kun Yang
To alleviate the shortage of computing power faced by clients in training deep neural networks (DNNs) using federated learning (FL), we leverage the edge computing and split learning to propose a model-splitting allowed FL (SFL) framework, with the aim to minimize the training latency without loss of test accuracy.
no code implementations • 8 Apr 2019 • Liang Wang, Peiqiu Huang, Kezhi Wang, Guopeng Zhang, Lei Zhang, Nauman Aslam, Kun Yang
In this paper, multi-unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC), i. e., UAVE is studied, where several UAVs are deployed as flying MEC platform to provide computing resource to ground user equipments (UEs).