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
相关论文
共 50 条
  • [41] A formulation-aid transfer learning-based framework in received power prediction
    Nguyen, Khanh N.
    Takizawa, Kenichi
    IEICE COMMUNICATIONS EXPRESS, 2022,
  • [42] A formulation-aid transfer learning-based framework in received power prediction
    Nguyen, Khanh N.
    Takizawa, Kenichi
    IEICE COMMUNICATIONS EXPRESS, 2023, 12 (02):
  • [43] FLITC: A Novel Federated Learning-Based Method for IoT Traffic Classification
    Abbasi, Mahmoud
    Taherkordi, Amir
    Shahraki, Amin
    2022 IEEE INTERNATIONAL CONFERENCE ON SMART COMPUTING (SMARTCOMP 2022), 2022, : 206 - 212
  • [44] Efficient federated transfer learning-based network anomaly detection for cooperative smart farming infrastructure
    Praharaj, Lopamudra
    Gupta, Deepti
    Gupta, Maanak
    SMART AGRICULTURAL TECHNOLOGY, 2025, 10
  • [45] Federated Learning-based Vehicle Trajectory Prediction against Cyberattacks
    Wang, Zhe
    Yan, Tingkai
    2023 IEEE 29TH INTERNATIONAL SYMPOSIUM ON LOCAL AND METROPOLITAN AREA NETWORKS, LANMAN, 2023,
  • [46] Citywide Wireless Traffic Prediction Based on Personalized Federated Learning
    Lin S.
    Ma J.
    Li Y.
    Zhuang B.
    Li T.
    Li Z.
    Tian J.
    Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China, 2023, 52 (01): : 67 - 73
  • [47] A Dynamic Underwater Sensor Network Architecture Based on Physical Clustering and Intra-cluster Autonomy
    Chen, Hainan
    Wu, Xiaoling
    Wang, Yanwen
    Liu, Guangcong
    Shu, Lei
    Zhang, Xiaobo
    ADVANCES IN WIRELESS SENSOR NETWORKS, CWSN 2013, 2014, 418 : 82 - 92
  • [48] Lightwave Power Transfer for Federated Learning-Based Wireless Networks
    Tran, Ha-Vu
    Kaddoum, Georges
    Elgala, Hany
    Abou-Rjeily, Chadi
    Kaushal, Hemani
    IEEE COMMUNICATIONS LETTERS, 2020, 24 (07) : 1472 - 1476
  • [49] An Intra-Cluster Trust-Based Secure Data Aggregation Framework for Wireless Sensor Networks
    Makin, Bhavna Arora
    Prof Devanand
    INTERNATIONAL JOURNAL OF NEXT-GENERATION COMPUTING, 2011, 2 (01): : 75 - 88
  • [50] Transfer learning-based default prediction model for consumer credit in China
    Li, Wei
    Ding, Shuai
    Chen, Yi
    Wang, Hao
    Yang, Shanlin
    JOURNAL OF SUPERCOMPUTING, 2019, 75 (02): : 862 - 884