A Decentralized Collaborative Learning Approach in 5G+ Core Networks

Document Type

Article

Publication Date

1-1-2024

Abstract

The 5G+ networks are going through extensive overhauling at both the radio access network (RAN) and core network (CN) to truly support the envisioned 5G+ use cases with ever so increasingly stringent quality of service requirements. Although many academic and industrial advancements have been realized, there are still needs for innovative changes vital for 5G+ connectivity, applications, and services. Consequently, the 3GPP is ushering in the next paradigm shift for CNs. CNs, which consist of several network functions (NFs) working cooperatively to service a RAN, are expected to exploit artificial intelligence (AI) in the form of an all-seeing-eye AI engine known as the Network Data Analytics Function (NWDAF). Its purpose is to optimize and automate network operation and management. However, accurate analytics require extensive machine learning (ML) training and as such, in this article, we present a decentralized collaborative ML approach to facilitate said training. Accordingly, we present two variations of a decentralized NWDAF architecture to enhance NF data localization, improve security, reduce control overhead during model training, shorten the training time, and finally, enhance the accuracy of the trained models by virtue of local testing on real-time network data. The first architecture decentralizes the global model training by exploiting the unique learning domain of each local model for each network function instance in a federated learning-based scheme while the second partitions the actual training network between multiple entities for each network function in a split learning-based scheme.

Identifier

85159713395 (Scopus)

Publication Title

IEEE Network

External Full Text Location

https://doi.org/10.1109/MNET.133.2200527

e-ISSN

1558156X

ISSN

08908044

First Page

288

Last Page

295

Issue

1

Volume

38

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