Joint Resource Allocation to Minimize Execution Time of Federated Learning in Cell-Free Massive MIMO

Due to its communication efficiency and privacy-preserving capability, federated learning (FL) has emerged as a promising framework for machine learning in 5G-and-beyond wireless networks. Of great interest is the design and optimization of new wireless network structures that support the stable and...

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Bibliographic Details
Published in:IEEE internet of things journal 2022-11, Vol.9 (21), p.21736-21750
Main Authors: Vu, Tung Thanh, Ngo, Duy Trong, Ngo, Hien Quoc, Dao, Minh Ngoc, Tran, Nguyen Hoang, Middleton, Richard H.
Format: Article
Language:English
Subjects:
Algorithms
Cell-free massive MIMO
Collaborative work
Communication
Computational modeling
Data models
Design optimization
execution time minimization
Federated learning
federated learning (FL)
Iterative methods
Machine learning
Optimization
Resource allocation
Servers
Symbols
Wireless networks
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Summary:Due to its communication efficiency and privacy-preserving capability, federated learning (FL) has emerged as a promising framework for machine learning in 5G-and-beyond wireless networks. Of great interest is the design and optimization of new wireless network structures that support the stable and fast operation of FL. Cell-free massive multiple-input-multiple-output (CFmMIMO) turns out to be a suitable candidate, which allows each communication round in the iterative FL process to be stably executed within a large-scale coherence time. Aiming to reduce the total execution time of the FL process in CFmMIMO, this article proposes choosing only a subset of available users to participate in FL. An optimal selection of users with favorable link conditions would minimize the execution time of each communication round while limiting the total number of communication rounds required. Toward this end, we formulate a joint optimization problem of user selection, transmit power, and processing frequency, subject to a predefined minimum number of participating users to guarantee the quality of learning. We then develop a new algorithm that is proven to converge to the neighborhood of the stationary points of the formulated problem. Numerical results confirm that our proposed approach significantly reduces the FL total execution time over baseline schemes. The time reduction is more pronounced when the density of access point deployments is moderately low.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2022.3183295