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Capsule Network Distributed Learning with Multi-Access Edge Computing for the Internet of Vehicles

For realizing an intelligent transport system, a vast amount of raw image data is required to train various intelligent applications on the Internet of Vehicles (IoV). A capsule network performs well in the computer vision area with fewer model parameters compared to convolutional neural networks. Due to the small-scale model, multi-access edge computing (MEC) devices can support online training for the whole capsule network model. In this article, we propose a novel framework for MEC-based capsule network (CapsMEC) distributed learning for IoV applications. Capsules in CapsMEC are specially designed to train in a collaborative way, which relieves the network pressure in MEC and the time-consuming training time of the traditional capsule network. Experimental results prove the effectiveness of the proposed framework.

Ketersediaan

Informasi Detail

Judul Seri

IEEE COMMUNICATIONS MAGAZINE; Vol.59 No.8 August 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 52 - 57

Bahasa

English

ISBN/ISSN

-

Klasifikasi

-

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

COMPUTER VISION
PERFORMANCE EVALUATION
COLLABORATION
DISTANCE LEARNING
DATA SCIENCE
COMPUTATIONAL MODELING
COMPUTER AIDED INSTRUCTION
TRAINING DATA

Info Detail Spesifik

DOI: 10.1109/MCOM.001.2001130

Pernyataan Tanggungjawab

Jie Xu, F. Richard Yu, Jingyu Wang, Qi Qi, Haifeng Sun, Jianxin Liao

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