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

• URL: http://arxiv.org/abs/2107.09577v1
• Date: Tue, 20 Jul 2021 15:46:53 GMT
• Title: How Does Cell-Free Massive MIMO Support Multiple Federated Learning Groups?
• Authors: Tung T. Vu, Hien Quoc Ngo, Thomas L. Marzetta, Michail Matthaiou
• Abstract summary: We propose a cell-free massive multiple-input multiple-output (MIMO) network to guarantee the stable operation of multiple FL processes. We then develop a novel scheme that asynchronously executes the iterations of FL processes under multicasting downlink and conventional uplink transmission protocols.
• Score: 42.63398054091038
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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