University of Oulu

J. Park et al., "Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications," in Proceedings of the IEEE, vol. 109, no. 5, pp. 796-819, May 2021, doi: 10.1109/JPROC.2021.3055679

Communication-efficient and distributed learning over wireless networks : principles and applications

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Author: Park, Jihong1; Samarakoon, Sumudu2; Elgabli, Anis2;
Organizations: 1School of Information Technology, Deakin University, VIC 3220, Geelong, Australia
2Centre for Wireless Communications, University of Oulu, Oulu 90014, Finland
3School of Electrical Engineering, Korea University, Seoul 02841, Korea
4School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea
5Université Paris-Saclay, CNRS, CentraleSupélec, Gifsur- Yvette 91190, France
6Lagrange Mathematical and Computing Research Center, Paris 75007, France
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 9.4 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2021-08-23


Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication systems and beyond. By imbuing intelligence into the network edge, edge nodes can proactively carry out decision-making and, thereby, react to local environmental changes and disturbances while experiencing zero communication latency. To achieve this goal, it is essential to cater for high ML inference accuracy at scale under the time-varying channel and network dynamics, by continuously exchanging fresh data and ML model updates in a distributed way. Taming this new kind of data traffic boils down to improving the communication efficiency of distributed learning by optimizing communication payload types, transmission techniques, and scheduling, as well as ML architectures, algorithms, and data processing methods. To this end, this article aims to provide a holistic overview of relevant communication and ML principles and, thereby, present communication-efficient and distributed learning frameworks with selected use cases.

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Series: Proceedings of the IEEE
ISSN: 0018-9219
ISSN-E: 1558-2256
ISSN-L: 0018-9219
Volume: 109
Issue: 5
Pages: 796 - 819
Article number: 9357490
DOI: 10.1109/JPROC.2021.3055679
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This work was supported in part by the INFOTECH Project NOOR, by the NEGEIN project, by the EU-CHISTERA projects LeadingEdge and CONNECT, by the EU-H2020 project (IntellIoT) and the Academy of Finland projects MISSION and SMARTER, and by the Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2018-0-00170, Virtual Presence in Moving Objects through 5G).
EU Grant Number: (957218) IntellIoT - Intelligent, distributed, human-centered and trustworthy IoT environments
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