Serving federated learning and non-federated learning users: a massive MIMO approach

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

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

Abstract

Federated learning (FL) with its data privacy protection and communication efficiency has been considered as a promising learning framework for beyond-5G/6G systems. We consider a scenario where a group of downlink non-FL users are jointly served with a group of FL users using massive multiple input multiple-output technology. The main challenge is how to utilise the resource to optimally serve both FL and non-FL users. We propose a communication scheme that serves the downlink of the non-FL users (UEs) and the uplink of FL UEs in each half of the frequency band. We formulate an optimization problem for optimizing transmit power to maximize the minimum effective data rates for non-FL users, while guaranteeing a quality-of service time of each FL communication round for FL users. Then, a successive convex approximation-based algorithm is proposed to solve the formulated problem. Numerical results confirm that our proposed scheme significantly outperforms the baseline scheme.
Original languageEnglish
Publication statusAccepted - 01 Jun 2022
Event2022 IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) - Oulu, Finland
Duration: 04 Jul 202206 Jul 2022

Conference

Conference2022 IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
Country/TerritoryFinland
CityOulu
Period04/07/202206/07/2022

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