Chinese Journal on Internet of Things ›› 2023, Vol. 7 ›› Issue (2): 50-66.doi: 10.11959/j.issn.2096-3750.2023.00323

• Theory and Technology • Previous Articles     Next Articles

A survey of federated learning for 6G networks

Guanglei GENG1,2, Bo GAO1,2, Ke XIONG1,2, Pingyi FAN3, Yang LU1,2, Yuwei WANG4   

  1. 1 School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
    2 Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education, Beijing Jiaotong University, Beijing 100044, China
    3 National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
    4 Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
  • Revised:2023-01-18 Online:2023-06-30 Published:2023-06-01
  • Supported by:
    The National Natural Science Foundation of China(61872028);The Fundamental Research Funds for the Central Universities(2021JBM008);The Fundamental Research Funds for the Central Universities(2022JBXT001)


It is an important feature of the 6G that how to realize everything interconnection through large-scale complex heterogeneous networks based on native artificial intelligence (AI).Thanks to the distinct machine learning architecture of data processing locally, federated learning (FL) is regarded as one of the promising solutions to incorporate distributed AI in 6G scenarios, and has become a critical research direction of 6G.Therefore, the necessity of introducing distributed AI into the future 6G especially for internet of things (IoT) scenarios was analyzed.And then, the potentials of FL in meeting the 6G requirements were discussed, and the state-of-the-arts of FL related technologies such as architecture design, resource utilization, data transmission, privacy protection, and service provided for 6G were investigated.Finally, several key technical challenges and potential valuable research directions for FL-empowered 6G were put forward.

Key words: 6G networks, internet of things, artificial intelligence, federated learning

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