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

• 作者: Zhaohui Yang Mingzhe Chen Kai-Kit Wong H.Vincent Poor Shuguang Cui
• 来源期刊: 工程（英文）
• 年，卷（期）: 2022,（1）
• 所属期刊栏目: 6G Requirements, Vision, and Enabling Technologies-Review
• 页码范围: 33-41
• 页数: 9页
• 语种: 英文

摘要

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 envi-sioned 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 communica-tions are detailed.The main problems and challenges associated with such applications are discussed.Finally,a comprehensive FL implementation for wireless communications is described.