Intra-Cluster Federated Learning-Based Model Transfer Framework for Traffic Prediction in Core Network

被引:7
|
作者
Li, Pengyu [1 ]
Shi, Yingji [2 ]
Xing, Yanxia [1 ]
Liao, Chaorui [2 ]
Yu, Menghan [1 ]
Guo, Chengwei [2 ]
Feng, Lei [2 ]
机构
[1] China Telecom Res Inst, 6G Res Ctr, Beijing 102209, Peoples R China
[2] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China
基金
国家重点研发计划;
关键词
traffic prediction; core network; federated learning;
D O I
10.3390/electronics11223793
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Accurate prediction of cellular traffic will contribute to efficient operations and management of mobile network. With deep learning, many studies have achieved exact cellular traffic prediction. However, the reality is that quite a few subnets in the core network do not have sufficient computing power to train their deep learning model, which we call subnets (LCP-Nets) with limited computing power. In order to improve the traffic prediction efficiency of LCP-Nets with the help of deep learning and the subnets (ACP-Nets) with abundant computing power under the requirement of privacy protection, this paper proposes an intra-cluster federated learning-based model transfer framework. This framework customizes models for LCP-Nets, leveraging transferring models trained by ACP-Nets. Experimental results on the public dataset show that the framework can improve the efficiency of LCP-Nets traffic prediction.
引用
收藏
页数:16
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