How Does Cell-Free Massive MIMO Support Multiple Federated Learning Groups?

Thanh T. Vu*, Hien Quoc Ngo, Thomas L. Marzetta, Michalis Matthaiou

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Federated learning (FL) has been considered as a promising learning framework for future machine learning systems due to its privacy preservation and communication efficiency. In beyond-5G/6G systems, it is likely to have multiple FL groups with different learning purposes. This scenario leads to a question: How does a wireless network support multiple FL groups? As an answer, we first propose to use a cell-free massive multiple-input multiple-output (MIMO) network to guarantee the stable operation of multiple FL processes by letting the iterations of these FL processes be executed together within a large-scale coherence time. We then develop a novel scheme that asynchronously executes the iterations of FL processes under multicasting downlink and conventional uplink transmission protocols. Finally, we propose a simple/low-complexity resource allocation algorithm which optimally chooses the power and computation resources to minimize the execution time of each iteration of each FL process.
Original languageEnglish
Title of host publication2021 IEEE SPAWC
Publisher IEEE
Publication statusAccepted - 01 Jul 2021

Publication series

NameIEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
PublisherIEEE
ISSN (Print)1948-3244
ISSN (Electronic)1948-3252

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