Massive MIMO for serving federated learning and non-federated learning users

Muhammad Farooq, Tung Thanh Vu, Hien Quoc Ngo, Le-Nam Tran

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Abstract

With its privacy preservation and communication efficiency, federated learning (FL) has emerged as a promising learning framework for beyond 5G wireless networks. It is anticipated that future wireless networks will jointly serve both FL and downlink non-FL user groups in the same time-frequency resource. While in the downlink of each FL iteration, both groups simultaneously receive data from the base station in the same time-frequency resource, the uplink of each FL iteration requires bidirectional communication to support uplink transmission for FL users and downlink transmission for non-FL users. To overcome this challenge, we present half-duplex (HD) and full-duplex (FD) communication schemes to serve both groups. More specifically, we adopt the massive multiple-input multiple-output technology and aim to maximize the minimum effective rate of non-FL users under a quality of service (QoS) latency constraint for FL users. Since the formulated problem is nonconvex, we propose a power control algorithm based on successive convex approximation to find a stationary solution. Numerical results show that the proposed solutions perform significantly better than the considered baselines schemes. Moreover, the FD-based scheme outperforms the HD-based counterpart in scenarios where the self-interference is small or moderate and/or the size of FL model updates is large.

Original languageEnglish
Number of pages16
JournalIEEE Transactions on Wireless Communications
Early online date23 May 2023
DOIs
Publication statusEarly online date - 23 May 2023

Bibliographical note

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Keywords

  • Data privacy
  • Downlink
  • Quality of service
  • Servers
  • Training
  • Uplink
  • Wireless networks

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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