Blockchain-Enabled 5G Edge Networks and Beyond: An Intelligent Cross-Silo Federated Learning Approach

被引:20
|
作者
Rahmadika, Sandi [1 ]
Firdaus, Muhammad [1 ]
Jang, Seolah [1 ]
Rhee, Kyung-Hyune [2 ]
机构
[1] Pukyong Natl Univ, Dept Artificial Intelligence Convergence, Busan 48513, South Korea
[2] Pukyong Natl Univ, Dept IT Convergence & Applicat Engn, Busan 48513, South Korea
基金
新加坡国家研究基金会;
关键词
INDUSTRIAL INTERNET;
D O I
10.1155/2021/5550153
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Edge networks (ENs) in 5G have the capability to protect traffic between edge entry points (edge-to-edge), enabling the design of various flexible and customizable applications. The advantage of edge networks is their pioneering integration of other prominent technologies such as blockchain and federated learning (FL) to produce better services on wireless networks. In this paper, we propose an intelligent system integrating blockchain technologies, 5G ENs, and FL to create an efficient and secure framework for transactions. FL enables user equipment (UE) to train the artificial intelligence model without exposing the UE's valuable data to the public, or to the model providers. Furthermore, the blockchain is an immutable data approach that can be leveraged for FL across 5G ENs and beyond. The recorded transactions cannot be altered maliciously, and they remain unchanged by design. We further propose a dynamic authentication protocol for UE to interact with a diverse base station. We apply blockchain as a reward mechanism in FL to enable computational offloading in wireless networks. Additionally, we implement and investigate blockchain technology for FL in 5G UE.
引用
收藏
页数:14
相关论文
共 50 条
  • [31] Efficient and Secure Data Sharing for 5G Flying Drones: A Blockchain-Enabled Approach
    Feng, Chaosheng
    Yu, Keping
    Bashir, Ali Kashif
    Al-Otaibi, Yasser D.
    Lu, Yang
    Chen, Shengbo
    Zhang, Di
    IEEE NETWORK, 2021, 35 (01): : 130 - 137
  • [32] Federated Learning and Blockchain-Enabled Intelligent Manufacturing for Sustainable Energy Production in Industry 4.0
    Sun, Fanglei
    Diao, Zhifeng
    PROCESSES, 2023, 11 (05)
  • [33] FedTwin: Blockchain-Enabled Adaptive Asynchronous Federated Learning for Digital Twin Networks
    Qu, Youyang
    Gao, Longxiang
    Xiang, Yong
    Shen, Shigen
    Yu, Shui
    IEEE NETWORK, 2022, 36 (06): : 183 - 190
  • [34] A Federated Learning and Blockchain-Enabled Sustainable Energy Trade at the Edge: A Framework for Industry 4.0
    Otoum, Safa
    Al Ridhawi, Ismaeel
    Mouftah, Hussein
    IEEE INTERNET OF THINGS JOURNAL, 2023, 10 (04) : 3018 - 3026
  • [35] Blockchain-Enabled Federated Learning for Enhanced Collaborative Intrusion Detection in Vehicular Edge Computing
    El Houda, Zakaria Abou
    Moudoud, Hajar
    Brik, Bouziane
    Khoukhi, Lyes
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2024, 25 (07) : 7661 - 7672
  • [36] Blockchain-enabled Edge Computing Framework for Hierarchic Cluster-based Federated Learning
    Huang, Xiaoge
    Wu, Yuhang
    Chen, Zhi
    Chen, Qianbin
    Zhang, Jie
    2022 14TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS AND SIGNAL PROCESSING, WCSP, 2022, : 33 - 37
  • [37] Distance-Aware Hierarchical Federated Learning in Blockchain-Enabled Edge Computing Network
    Huang, Xiaoge
    Wu, Yuhang
    Liang, Chengchao
    Chen, Qianbin
    Zhang, Jie
    IEEE INTERNET OF THINGS JOURNAL, 2023, 10 (21) : 19163 - 19176
  • [38] Enabling Intelligent IoCV Services at the Edge for 5G Networks and Beyond
    Al Ridhawi, Ismaeel
    Aloqaily, Moayad
    Boukerche, Azzedine
    Jararweh, Yaser
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2021, 22 (08) : 5190 - 5200
  • [39] Secure Blockchain-Enabled Authentication Key Management Framework with Big Data Analytics for Drones in Networks Beyond 5G Applications
    Mishra, Amit Kumar
    Wazid, Mohammad
    Singh, Devesh Pratap
    Das, Ashok Kumar
    Singh, Jaskaran
    Vasilakos, Athanasios V.
    DRONES, 2023, 7 (08)
  • [40] Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioning
    Das, Anirban
    Castiglia, Timothy
    Wang, Shiqiang
    Patterson, Stacy
    ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2022, 13 (06)