A Hierarchical Networking and Privacy-Preserving Federated Learning Framework for 5G Networks  

作　　者：Chen Guo Fang Cui Chao Xu Mohan Su Zhihao Wang Hongjia Li 

机构地区：[1]China Mobile Group Device Co.,Ltd,Beijing 100053,China [2]China Mobile Communications Group Co.,Ltd,Beijing 100053,China [3]Institute of Information Engineering,Chinese Academy of Sciences(CAS),Beijing 100085,China [4]University of Chinese Academy of Sciences(UCAS),Beijing 101408,China

出　　处：《Journal of Communications and Information Networks》2025年第1期26-36,共11页通信与信息网络学报(英文)

基　　金：supported by the Climbing Program of Institute of Information Engineering,Chinese Academy of Sciences under Grant E3Z0031.

摘　　要：Artificial intelligence(AI)has been widely envisioned as a key enabler for 5G and beyond networks.To integrate AI into mobile networks,the third generation partnership(3GPP)introduces the network data analytics function(NWDAF)starting from Release 15 to support“in-network”learning and inference,and further supports federated learning(FL)from Release 16 to protect data privacy.However,practical deployment of federated learning in 5G networks still faces challenges of high communication overhead and potential risks of model and data leakage.Motivated by these challenges,we propose a hierarchical networking and privacy-preserving federated learning(HiNP-FL)framework for 5G networks.Specifically,in the HiNP-FL framework,1)we propose the hierarchical NWDAF based FL mechanism to reduce FL communication overhead in 5G networks;2)based on multi-party polynomial evaluation(OMPE),we design a FL model and data privacy protection mechanism for the hierarchical FL mechanism;3)we validate the privacy protection capability of the HiNP-FL framework through privacy analysis,and testify its effectiveness in terms of model accuracy and communication efficiency through extensive experiments.

关 键 词：5G network federated learning privacy preservation communication overhead hierarchical architecture 

分 类 号：TN929.5[电子电信—通信与信息系统]