Search Results for author: Jia Hu

Found 11 papers, 5 papers with code

Faster Federated Learning with Decaying Number of Local SGD Steps

no code implementations16 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.

Federated Learning

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 in Edge Computing

no code implementations12 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.

Autonomous Driving Continuous Control +8

Accelerating Federated Learning with a Global Biased Optimiser

1 code implementation20 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.

Federated Learning

Online Service Migration in Mobile Edge with Incomplete System Information: A Deep Recurrent Actor-Critic Learning Approach

1 code implementation16 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.

Cloud Computing 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 +2

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

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