Federated Learning for 6G: Applications, Challenges, and Opportunities

Zhaohui Yang, Mingzhe Chen, Kai Kit Wong, H. Vincent Poor, Shuguang Cui

Research output: Contribution to journalReview articlepeer-review

107 Scopus citations


Standard machine-learning approaches involve the centralization of training data in a data center, where centralized machine-learning algorithms can be applied for data analysis and inference. However, due to privacy restrictions and limited communication resources in wireless networks, it is often undesirable or impractical for the devices to transmit data to parameter sever. One approach to mitigate these problems is federated learning (FL), which enables the devices to train a common machine learning model without data sharing and transmission. This paper provides a comprehensive overview of FL applications for envisioned sixth generation (6G) wireless networks. In particular, the essential requirements for applying FL to wireless communications are first described. Then potential FL applications in wireless communications are detailed. The main problems and challenges associated with such applications are discussed. Finally, a comprehensive FL implementation for wireless communications is described.

Original languageEnglish (US)
Pages (from-to)33-41
Number of pages9
StatePublished - Jan 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Environmental Engineering
  • General Chemical Engineering
  • Materials Science (miscellaneous)
  • Energy Engineering and Power Technology
  • General Engineering


  • 6G
  • Communication and computing
  • Federated learning
  • Reconfigurable intelligent surface
  • Semantic communication
  • Sensing


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