no code implementations • 1 Apr 2020 • Zheyi Chen, Jia Hu, Geyong Min, Albert Y. Zomaya, Tarek El-Ghazawi
Resource provisioning for cloud computing necessitates the adaptive and accurate prediction of cloud workloads.
no code implementations • 7 Apr 2020 • You Li, Yuan Zhuang, Xin Hu, Zhouzheng Gao, Jia Hu, Long Chen, Zhe He, Ling Pei, Kejie Chen, Maosong Wang, Xiaoji Niu, Ruizhi Chen, John Thompson, Fadhel Ghannouchi, Naser El-Sheimy
Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges.
Networking and Internet Architecture Signal Processing
no code implementations • 6 Jun 2020 • Xin Hu, Zhijun Liu, You Li, Lexi Xu, Sun Zhang, Qinlong Li, Jia Hu, WenHua Chen, Weidong Wang, Mohamed Helaoui, Fadhel M. Ghannouchi
In this work, a low complexity, general architecture based on the deep real-valued convolutional neural network (DRVCNN) is proposed to build the nonlinear behavior of the power amplifier.
1 code implementation • 1 Jul 2020 • Jed Mills, Jia Hu, Geyong Min
The rapidly expanding number of Internet of Things (IoT) devices is generating huge quantities of data, but public concern over data privacy means users are apprehensive to send data to a central server for machine learning (ML) purposes.
1 code implementation • 17 Jul 2020 • Jed Mills, Jia Hu, Geyong Min
MTFL is compatible with popular iterative FL optimisation algorithms such as Federated Averaging (FedAvg), and we show empirically that a distributed form of Adam optimisation (FedAvg-Adam) benefits convergence speed even further when used as the optimisation strategy within MTFL.
1 code implementation • 5 Aug 2020 • Jin Wang, Jia Hu, Geyong Min, Albert Y. Zomaya, Nektarios Georgalas
Recently, many deep reinforcement learning (DRL) based methods have been proposed to learn offloading policies through interacting with the MEC environment that consists of UE, wireless channels, and MEC hosts.
1 code implementation • 16 Dec 2020 • Jin Wang, Jia Hu, Geyong Min, Qiang Ni, Tarek El-Ghazawi
To address these challenges, we propose a novel learning-driven method, which is user-centric and can make effective online migration decisions by utilizing incomplete system-level information.
1 code implementation • 20 Aug 2021 • Jed Mills, Jia Hu, Geyong Min, Rui Jin, Siwei Zheng, Jin Wang
To address this challenge, we propose a novel, generalised approach for incorporating adaptive optimisation into FL with the Federated Global Biased Optimiser (FedGBO) algorithm.
no code implementations • 12 Sep 2021 • Jin Wang, Jia Hu, Jed Mills, Geyong Min, Ming Xia
Federated learning (FL) is a privacy-preserving distributed machine learning paradigm that enables collaborative training among geographically distributed and heterogeneous devices without gathering their data.
no code implementations • 9 Dec 2021 • Yifan Zhuang, Ziyuan Pu, Jia Hu, Yinhai Wang
Besides, the quantized IT-MN achieves an inference time of 0. 21 seconds per image pair on the edge device, which also demonstrates the potentiality of deploying the proposed model on edge devices as a highly efficient pedestrian detection algorithm.
no code implementations • 16 May 2023 • Jed Mills, Jia Hu, Geyong Min
FedAvg can improve the communication-efficiency of training by performing more steps of Stochastic Gradient Descent (SGD) on clients in each round.