Toward Knowledge as a Service over Networks : A Deep Learning Model Communication Paradigm

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

15 Scopus Citations
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Author(s)

  • Ziqian Chen
  • Ling-Yu Duan
  • Yihang Lou
  • Tiejun Huang
  • Wen Gao

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)1349-1363
Journal / PublicationIEEE Journal on Selected Areas in Communications
Volume37
Issue number6
Online published14 Mar 2019
Publication statusPublished - Jun 2019

Abstract

The advent of artificial intelligence and Internet of Things has led to the seamless transition turning the big data into the big knowledge. The deep learning models, which assimilate knowledge from large-scale data, can be regarded as an alternative but promising modality of knowledge for artificial intelligence services. Yet, the compression, storage, and communication of the deep learning models towards better knowledge services, especially over networks, pose a set of challenging problems on both industrial and academic realms. This paper presents the deep learning model communication paradigm based on multiple model compression, which greatly exploits the redundancy among multiple deep learning models in different application scenarios. We analyze the potential and demonstrate the promise of the compression strategy for deep learning model communication through a set of experiments. Moreover, the interoperability in deep learning model communication, which is enabled based on the standardization of compact deep learning model representation, is also discussed and envisioned.

Research Area(s)

  • Deep learning, deep learning model communication, knowledge centric network, neural network compression

Citation Format(s)

Toward Knowledge as a Service over Networks : A Deep Learning Model Communication Paradigm. / Chen, Ziqian; Duan, Ling-Yu; Wang, Shiqi; Lou, Yihang; Huang, Tiejun; Wu, Dapeng Oliver; Gao, Wen.

In: IEEE Journal on Selected Areas in Communications, Vol. 37, No. 6, 06.2019, p. 1349-1363.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review