- Title
- Cell-free massive MIMO for wireless federated learning
- Creator
- Vu, Tung; Ngo, Duy; Tran, Nguyen; Ngo, Hien; Dao, Minh; Middleton, Richard
- Date
- 2020
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/184682
- Identifier
- vital:16557
- Identifier
-
https://doi.org/10.1109/TWC.2020.3002988
- Identifier
- ISBN:1536-1276 (ISSN)
- Abstract
- 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.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Transactions on Wireless Communications Vol. 19, no. 10 (2020), p. 6377-6392
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2020 IEEE
- Rights
- Open Access
- Subject
- 4006 Communications Engineering; 4606 Distributed Computing and Systems Software; Cell-free massive MIMO; Federated learning
- Full Text
- Reviewed
- Funder
- The work of Tung T. Vu was supported by the ECR-HDR Scholarship from The University of Newcastle. The work of Duy T. Ngo was supported in part by the Australian Research Council Discovery Project (ARCDP) under Grant DP170100939 and in part by the Vietnam National Foundation for Science and Technology Development (NAFOSTED) under Grant 102.02-2018.320. The work of Nguyen H. Tran was supported in part by the ARCDP under Grant DP200103718 and in part by NAFOSTED under Grant 102.02-2019.321. The work of Hien Quoc Ngo was supported by the U.K. Research and Innovation Future Leaders Fellowships under Grant MR/S017666/1. The work of Minh N. Dao was supported in part by ARCDP under Grant DP160101537 and Grant DP190100555.
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