@inproceedings{58607f64b29646e997f88c43134eb33f,
title = "A Distributed NWDAF Architecture for Federated Learning in 5G",
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. ",
keywords = "5G, core networks, data analytics, federated learning, NWDAF",
author = "Youbin Jeon and Hyeonjae Jeong and Sangwon Seo and Taeyun Kim and Haneul Ko and Sangheon Pack",
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: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Consumer Electronics, ICCE 2022 ; Conference date: 07-01-2022 Through 09-01-2022",
year = "2022",
doi = "10.1109/ICCE53296.2022.9730220",
language = "English",
series = "Digest of Technical Papers - IEEE International Conference on Consumer Electronics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 IEEE International Conference on Consumer Electronics, ICCE 2022",
}