@inproceedings{00f5864b6e4640d5baef655920a7d611,
title = "Serving federated learning and non-federated learning users: a massive MIMO approach",
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.",
author = "Muhammad Farooq and Vu, {Thanh Tung} and Hien-Quoc Ngo and Le-Nam Tran",
year = "2022",
month = jul,
doi = "10.1109/SPAWC51304.2022.9833955",
language = "English",
series = "IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)",
publisher = "IEEE Xplore",
booktitle = "2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC): Proceedings",
note = "2022 IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) ; Conference date: 04-07-2022 Through 06-07-2022",
}