Federated Learning in Multi-antenna Wireless Networks

Zhendong Song, Hongguang Sun, Howard H. Yang, Xijun Wang, Tony Q.S. Quek

研究成果: Conference contribution

4 引文 斯高帕斯(Scopus)

摘要

In this work, we propose an analytical model to study the convergence performance of federated learning (FL) in a multi-antenna wireless network by comparing three different scheduling policies, i.e., the round robin (RR), random scheduling (RS), and proportional fair (PF). We derive tractable expressions for the convergence rates of FL by taking into account the scheduling policy, antenna number, channel fading, and intercell interference. Our results show that PF achieves the best convergence performance under the high signal-to-interference-plus-noise ratio (SINR) threshold, while the three scheduling policies achieve very similar convergence rates under extremely low SINR threshold. Given the number of antennas per base station (BS), we observe an optimal number of scheduled user equipments (UEs) per BS that maximizes the convergence rate of FL under high SINR threshold because of the trade-off between serving more UEs and achieving higher successful transmission probability.

原文English
主出版物標題2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728194417
DOIs
出版狀態Published - 2021 6月
事件2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Virtual, Online
持續時間: 2021 6月 142021 6月 23

出版系列

名字2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings

Conference

Conference2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021
城市Virtual, Online
期間21-06-1421-06-23

All Science Journal Classification (ASJC) codes

  • 人工智慧
  • 電腦網路與通信
  • 硬體和架構
  • 資訊系統與管理

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