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

By Zhaohui Yang, Mingzhe Chen, Kai‐Kit Wong, H. Vincent Poor, Shuguang Cui. Engineering, 2021

344 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/73d2584fbcd38a636b1e770c59aaffbc6f80dd51

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In Artificial Intelligence

In Electrical and Electronic Engineering

Citations in the corpus

Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services (2023, background): “For instance, users can assess the reputation of AIGC service providers based on their transaction history to promote service optimization and improvement.”
Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, unknown): “heavy burden to the already congested wireless communication networks [16].”
Decentralized Federated Learning: A Survey and Perspective (2023, unknown): “…its excellent capabilities in various areas, including intelligent transportation, Internet of Things (IoT), healthcare, manufacturing, agriculture, energy, remote sensing, and more [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28].”
Federated Learning in Edge Computing: A Systematic Survey (2022, background): “…communications perspective X X X X [92] Survey on FL-powered IoT applications to run on IoT networks X X X [93] Survey on the FL-enabled IIoT X X [94] Survey on FL implementation in wireless communications X X X X [95] Analysis of FL’s potential for enabling a wide range of IoT services X X [96]…”
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges (2021, unknown): “In [8]–[12], the authors provided compre-”
6G Internet of Things: A Comprehensive Survey (2021, background): “Another case study is explored in [60], where UAVs are employed to realize UAV-to-Everything communications for supporting different data transmission solutions in IoT data sensing, emergency search and monitoring, and video streaming in wireless 6G scenarios.”