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

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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 publicationProceedings of the IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages401-405
ISBN (Electronic)9781665428514
ISBN (Print)9781665428521
DOIs
Publication statusPublished - 12 Nov 2021

Publication series

NameIEEE International Workshop on Signal Processing Advances in Wireless Communications: Proceedings
ISSN (Print)1948-3244
ISSN (Electronic)1948-3252

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