- Title
- Joint Resource Allocation to Minimize Execution Time of Federated Learning in Cell-Free Massive MIMO
- Creator
- Vu, Tung Thanh; Ngo, Duy Trong; Ngo, Hien Quoc; Dao, Minh Ngoc; Tran, Nguyen Hoang; Middleton, Richard H.
- Relation
- IEEE Internet of Things Journal Vol. 9, Issue 21, p. 21736-21750
- Publisher Link
- http://dx.doi.org/10.1109/jiot.2022.3183295
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- journal article
- Date
- 2022
- Description
- 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.
- Subject
- cell-free massive MIMO; execution time minimization; federated learning (FL); machine learning
- Identifier
- http://hdl.handle.net/1959.13/1489280
- Identifier
- uon:52672
- Identifier
- ISSN:2327-4662
- Language
- eng
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