Mobile Communication Resource Management Based on Federated Learning: Methods, Progress and Prospect [基于联邦学习的移动通信资源管理:方法, 进展与展望] - Details

Author：

Sun, E. (Sun, E..) | Zhang, H. (Zhang, H..) | He, R. (He, R..) | Zhang, D. (Zhang, D..) | Zhang, Y. (Zhang, Y..)

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Scopus

Abstract：

Federated　learning　(FL)　has　the　characteristic　of　implementing　model　training　without　data　sharing　and　operating　effective　resource　management　while　protecting　data　privacy.　Therefore,　it　has　become　one　of　the　research　hotspots　in　the　field　of　mobile　communication　resource　management.　In　this　survey,　the　algorithms,　progress　and　future　trends　of　FL　in　mobile　communication　resource　management　were　summarized　and　analyzed.　First,　the　basic　concept　of　FL　was　introduced.　Then,　the　performance　of　FL　resource　management　methods　in　distributed　wireless　network,　mobile　edge　network,　Internet　of　vehicles,　fog　radio　access　network,　and　ultra　dense　network　scenarios　were　discussed,　and　their　advantages　and　disadvantages　were　analyzed.　Based　on　the　progress　of　FL,　the　open　issues　of　FL　were　analyzed,　and　the　possible　solutions　were　proposed.　Finally,　the　potential　development　trends　of　FL　in　the　field　of　mobile　communication　resource　management　were　prospected.　©　2022,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.

Keyword：

Data privacy; Federated learning (FL); Machine learning; Mobile communication; Resource management; Shared data

Author Community：

[ 1 ] [Sun, E.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 2 ] [Zhang, H.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 3 ] [He, R.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 4 ] [Zhang, D.]Information Technology Support Center, Beijing University of Technology, Beijing, 100124, China
[ 5 ] [Zhang, Y.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 6 ] [Zhang, Y.]Beijing Laboratory of Advanced Information Networks, Beijing, 100124, China

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Source ：

Journal of Beijing University of Technology

ISSN： 0254-0037

Year： 2022

Issue： 7

Volume： 48

Page： 783-793

Cited Count：

WoS CC Cited Count： 0

SCOPUS Cited Count：

ESI Highly Cited Papers on the List： 0 Unfold All

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Chinese Cited Count：

30 Days PV： 3

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