A Distributed NWDAF Architecture for Federated Learning in 5G

Youbin Jeon, Hyeonjae Jeong, Sangwon Seo, Taeyun Kim, Haneul Ko, Sangheon Pack

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

For network automation and intelligence in 5G, the network data analytics function (NWDAF) has been introduced as a new network function. However, the existing centralized NWDAF structure can be overloaded if an amount of analytic data are concentrated. In this paper, we introduce a distributed NWDAF structure tailored for federated learning (FL) in 5G. Leaf NWDAFs create local models and root NWDAF construct a global model by aggregating the local models. This structure can guarantee data privacy since local models are created in NF, and can reduce network resource usage because the global model is created by collecting local models.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Consumer Electronics, ICCE 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665441544
DOIs
Publication statusPublished - 2022
Event2022 IEEE International Conference on Consumer Electronics, ICCE 2022 - Virtual, Online, United States
Duration: 2022 Jan 72022 Jan 9

Publication series

NameDigest of Technical Papers - IEEE International Conference on Consumer Electronics
Volume2022-January
ISSN (Print)0747-668X

Conference

Conference2022 IEEE International Conference on Consumer Electronics, ICCE 2022
Country/TerritoryUnited States
CityVirtual, Online
Period22/1/722/1/9

Keywords

  • 5G
  • core networks
  • data analytics
  • federated learning
  • NWDAF

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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