A Distributed NWDAF Architecture for Federated Learning in 5G

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

doi:10.1109/ICCE53296.2022.9730220

A Distributed NWDAF Architecture for Federated Learning in 5G

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

School of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

17 Citations (Scopus)

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 language	English
Title of host publication	2022 IEEE International Conference on Consumer Electronics, ICCE 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665441544
DOIs	https://doi.org/10.1109/ICCE53296.2022.9730220
Publication status	Published - 2022
Event	2022 IEEE International Conference on Consumer Electronics, ICCE 2022 - Virtual, Online, United States Duration: 2022 Jan 7 → 2022 Jan 9

Publication series

Name	Digest of Technical Papers - IEEE International Conference on Consumer Electronics
Volume	2022-January
ISSN (Print)	0747-668X

Conference

Conference	2022 IEEE International Conference on Consumer Electronics, ICCE 2022
Country/Territory	United States
City	Virtual, Online
Period	22/1/7 → 22/1/9

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work was supported in part by Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea Government (MSIT) (No. 2021-0-00739) and in part by National Research Foundation (NRF) of Korea Grant funded by the Korean Government (MSIT) (No. 2021R1A4A3022102).

Publisher Copyright:
© 2022 IEEE.

Keywords

5G
core networks
data analytics
federated learning
NWDAF

ASJC Scopus subject areas

Industrial and Manufacturing Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/ICCE53296.2022.9730220

Cite this

Jeon, Y., Jeong, H., Seo, S., Kim, T., Ko, H., & Pack, S. (2022). A Distributed NWDAF Architecture for Federated Learning in 5G. In 2022 IEEE International Conference on Consumer Electronics, ICCE 2022 (Digest of Technical Papers - IEEE International Conference on Consumer Electronics; Vol. 2022-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE53296.2022.9730220

A Distributed NWDAF Architecture for Federated Learning in 5G. / Jeon, Youbin; Jeong, Hyeonjae; Seo, Sangwon et al.
2022 IEEE International Conference on Consumer Electronics, ICCE 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (Digest of Technical Papers - IEEE International Conference on Consumer Electronics; Vol. 2022-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Jeon, Y, Jeong, H, Seo, S, Kim, T, Ko, H & Pack, S 2022, A Distributed NWDAF Architecture for Federated Learning in 5G. in 2022 IEEE International Conference on Consumer Electronics, ICCE 2022. Digest of Technical Papers - IEEE International Conference on Consumer Electronics, vol. 2022-January, Institute of Electrical and Electronics Engineers Inc., 2022 IEEE International Conference on Consumer Electronics, ICCE 2022, Virtual, Online, United States, 22/1/7. https://doi.org/10.1109/ICCE53296.2022.9730220

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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. ",

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A Distributed NWDAF Architecture for Federated Learning in 5G

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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