University of Oulu

M. Chen et al., "Distributed Learning in Wireless Networks: Recent Progress and Future Challenges," in IEEE Journal on Selected Areas in Communications, vol. 39, no. 12, pp. 3579-3605, Dec. 2021, doi: 10.1109/JSAC.2021.3118346

Distributed learning in wireless networks : recent progress and future challenges

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Author: Chen, Mingzhe1; Gündüz, Deniz2; Huang, Kaibin3;
Organizations: 1Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544 USA
2Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, U.K.
3Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong
4Department of Computer Science and Engineering, Kyung Hee University, Yongin 17104, South Korea
5Department of Communications Engineering, University of Oulu, 90014 Oulu, Finland
6Ericsson Research, 16483 Stockholm, Sweden
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 3.3 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2022-02-23


The next-generation of wireless networks will enable many machine learning (ML) tools and applications to efficiently analyze various types of data collected by edge devices for inference, autonomy, and decision making purposes. However, due to resource constraints, delay limitations, and privacy challenges, edge devices cannot offload their entire collected datasets to a cloud server for centrally training their ML models or inference purposes. To overcome these challenges, distributed learning and inference techniques have been proposed as a means to enable edge devices to collaboratively train ML models without raw data exchanges, thus reducing the communication overhead and latency as well as improving data privacy. However, deploying distributed learning over wireless networks faces several challenges including the uncertain wireless environment (e.g., dynamic channel and interference), limited wireless resources (e.g., transmit power and radio spectrum), and hardware resources (e.g., computational power). This paper provides a comprehensive study of how distributed learning can be efficiently and effectively deployed over wireless edge networks. We present a detailed overview of several emerging distributed learning paradigms, including federated learning, federated distillation, distributed inference, and multi-agent reinforcement learning. For each learning framework, we first introduce the motivation for deploying it over wireless networks. Then, we present a detailed literature review on the use of communication techniques for its efficient deployment. We then introduce an illustrative example to show how to optimize wireless networks to improve its performance. Finally, we introduce future research opportunities. In a nutshell, this paper provides a holistic set of guidelines on how to deploy a broad range of distributed learning frameworks over real-world wireless communication networks.

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Series: IEEE journal on selected areas in communications
ISSN: 0733-8716
ISSN-E: 1558-0008
ISSN-L: 0733-8716
Volume: 39
Issue: 12
Pages: 3579 - 3605
DOI: 10.1109/JSAC.2021.3118346
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: The work of Mingzhe Chen and H. Vincent Poor was supported by the U.S. National Science Foundation under Grant CCF-1908308. The work of Deniz Gündüz was supported in part by the European Research Council (ERC) through the Starting Grant BEACON 677854 and in part by the U.K. Engineering and Physical Sciences Research Council (EPSRC) through the CHIST-ERA Program under Grant EP/T023600/1. The work of Walid Saad was supported by the Office of Naval Research (ONR) under MURI Grant N00014-19-1-2621. The work of Mehdi Bennis was supported in part by the Academy of Finland 6G Flagship under Grant 318927, in part by the project SMARTER, in part by the projects EU-ICT IntellIoT and EUCHISTERA LearningEdge, and in part by CONNECT, Infotech-NOOR, and NEGEIN.
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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