Search Results for author: Jia Hu

Found 11 papers, 3 papers with code

Illumination and Temperature-Aware Multispectral Networks for Edge-Computing-Enabled Pedestrian Detection

no code implementations9 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.

Edge-computing Pedestrian Detection +1

Federated Ensemble Model-based Reinforcement Learning

no code implementations12 Sep 2021 Jin Wang, Jia Hu, Jed Mills, Geyong Min

Federated learning (FL) is a privacy-preserving machine learning paradigm that enables collaborative training among geographically distributed and heterogeneous users without gathering their data.

Autonomous Driving Continuous Control +5

Accelerating Federated Learning with a Global Biased Optimiser

no code implementations20 Aug 2021 Jed Mills, Jia Hu, Geyong Min, Rui Jin, Siwei Zheng, Jin Wang

Federated Learning (FL) is a recent development in the field of machine learning that collaboratively trains models without the training data leaving client devices, to preserve data privacy.

Federated Learning

Faster Federated Learning with Decaying Number of Local SGD Steps

no code implementations1 Jan 2021 Jed Mills, Jia Hu, Geyong Min

We propose instead decaying the number of local SGD steps, $K$, that clients perform during training rounds to allow minimisation of the true loss.

Federated Learning

Online Service Migration in Edge Computing with Incomplete Information: A Deep Recurrent Actor-Critic Method

no code implementations16 Dec 2020 Jin Wang, Jia Hu, Geyong Min

Multi-access Edge Computing (MEC) is an emerging computing paradigm that extends cloud computing to the network edge (e. g., base stations, MEC servers) to support resource-intensive applications on mobile devices.

Edge-computing

Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement Learning

1 code implementation5 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.

Edge-computing Meta Reinforcement Learning +1

Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing

1 code implementation17 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.

Edge-computing Federated Learning

Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT

1 code implementation1 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.

Edge-computing Federated Learning

A General Architecture for Behavior Modeling of Nonlinear Power Amplifier using Deep Convolutional Neural Network

no code implementations6 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.

Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation

no code implementations7 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

Towards Accurate Prediction for High-Dimensional and Highly-Variable Cloud Workloads with Deep Learning

no code implementations1 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.

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