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

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

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

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arxiv 2101.01338 v1 pith:VANP2UIL submitted 2021-01-05 cs.IT math.IT

Zhaohui Yang , Mingzhe Chen , Kai-Kit Wong , H. Vincent Poor , Shuguang Cui This is my paper · ORCID

classification cs.ITmath.IT

keywords wirelesslearningcommunicationsapplicationschallengescomprehensivedatafederated

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Traditional machine learning is centralized in the cloud (data centers). Recently, the security concern and the availability of abundant data and computation resources in wireless networks are pushing the deployment of learning algorithms towards the network edge. This has led to the emergence of a fast growing area, called federated learning (FL), which integrates two originally decoupled areas: wireless communication and machine learning. In this paper, we provide a comprehensive study on the applications of FL for sixth generation (6G) wireless networks. First, we discuss the key requirements in applying FL for wireless communications. Then, we focus on the motivating application of FL for wireless communications. We identify the main problems, challenges, and provide a comprehensive treatment of implementing FL techniques for wireless communications.

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A thematic survey maps the 6G literature onto the IMT-2030 framework's five dimensions and finds that 69.9% of screened papers address technology enablers.

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