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

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...

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Bibliographic Details
Main Authors: Vu, Tung T., Quoc Ngo, Hien, Marzetta, Thomas L., Matthaiou, Michail
Format: Conference Proceeding
Language:English
Subjects:
Approximation algorithms
Collaborative work
Signal processing
Signal processing algorithms
Time-frequency analysis
Training
Wireless networks
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Summary: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.
ISSN:1948-3252
DOI:10.1109/SPAWC51858.2021.9593248