Cell-free massive MIMO for wireless federated learning
- Vu, Tung, Ngo, Duy, Tran, Nguyen, Ngo, Hien, Dao, Minh, Middleton, Richard
- Authors: Vu, Tung , Ngo, Duy , Tran, Nguyen , Ngo, Hien , Dao, Minh , Middleton, Richard
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Transactions on Wireless Communications Vol. 19, no. 10 (2020), p. 6377-6392
- Full Text:
- Reviewed:
- Description: This paper proposes a novel scheme for cell-free massive multiple-input multiple-output (CFmMIMO) networks to support any federated learning (FL) framework. This scheme allows each instead of all the iterations of the FL framework to happen in a large-scale coherence time to guarantee a stable operation of an FL process. To show how to optimize the FL performance using this proposed scheme, we consider an existing FL framework as an example and target FL training time minimization for this framework. An optimization problem is then formulated to jointly optimize the local accuracy, transmit power, data rate, and users' processing frequency. This mixed-Timescale stochastic nonconvex problem captures the complex interactions among the training time, and transmission and computation of training updates of one FL process. By employing the online successive convex approximation approach, we develop a new algorithm to solve the formulated problem with proven convergence to the neighbourhood of its stationary points. Our numerical results confirm that the presented joint design reduces the training time by up to 55% over baseline approaches. They also show that CFmMIMO here requires the lowest training time for FL processes compared with cell-free time-division multiple access massive MIMO and collocated massive MIMO. © 2002-2012 IEEE.
- Authors: Vu, Tung , Ngo, Duy , Tran, Nguyen , Ngo, Hien , Dao, Minh , Middleton, Richard
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Transactions on Wireless Communications Vol. 19, no. 10 (2020), p. 6377-6392
- Full Text:
- Reviewed:
- Description: This paper proposes a novel scheme for cell-free massive multiple-input multiple-output (CFmMIMO) networks to support any federated learning (FL) framework. This scheme allows each instead of all the iterations of the FL framework to happen in a large-scale coherence time to guarantee a stable operation of an FL process. To show how to optimize the FL performance using this proposed scheme, we consider an existing FL framework as an example and target FL training time minimization for this framework. An optimization problem is then formulated to jointly optimize the local accuracy, transmit power, data rate, and users' processing frequency. This mixed-Timescale stochastic nonconvex problem captures the complex interactions among the training time, and transmission and computation of training updates of one FL process. By employing the online successive convex approximation approach, we develop a new algorithm to solve the formulated problem with proven convergence to the neighbourhood of its stationary points. Our numerical results confirm that the presented joint design reduces the training time by up to 55% over baseline approaches. They also show that CFmMIMO here requires the lowest training time for FL processes compared with cell-free time-division multiple access massive MIMO and collocated massive MIMO. © 2002-2012 IEEE.
Joint resource allocation to minimize execution time of federated learning in cell-free massive MIMO
- Vu, Tung, Ngo, Duy, Ngo, Hien, Dao, Minh, Tran, Nguyen, Middleton, Richard
- Authors: Vu, Tung , Ngo, Duy , Ngo, Hien , Dao, Minh , Tran, Nguyen , Middleton, Richard
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Internet of Things Journal Vol. 9, no. 21 (2022), p. 21736-21750
- Full Text:
- Reviewed:
- 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. © 2014 IEEE.
Joint resource allocation to minimize execution time of federated learning in cell-free massive MIMO
- Authors: Vu, Tung , Ngo, Duy , Ngo, Hien , Dao, Minh , Tran, Nguyen , Middleton, Richard
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Internet of Things Journal Vol. 9, no. 21 (2022), p. 21736-21750
- Full Text:
- Reviewed:
- 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. © 2014 IEEE.
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