Collocated Massive MIMO SWIPT for Wireless Federated Learning
Kesargheh, Mohammad Mansour; Zahedi, Abdulhamid; Rasti, Mehdi (2024-11-05)
IEEE
05.11.2024
M. M. Kesargheh, A. Zahedi and M. Rasti, "Collocated Massive MIMO SWIPT for Wireless Federated Learning," in IEEE Transactions on Green Communications and Networking, vol. 9, no. 3, pp. 1385-1397, Sept. 2025, doi: 10.1109/TGCN.2024.3491636
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202503101921
https://urn.fi/URN:NBN:fi:oulu-202503101921
Tiivistelmä
Abstract
This paper explores the application of federated learning (FL) in 5G and 6G wireless networks, focusing on achieving a global model from user-local datasets. To enhance efficiency, we investigate minimizing training time within the collocated massive multiple-input multiple-output (CL-mMIMO) network framework. We propose an online successive convex approximation (SCA) approach to decompose the stochastic nonconvex problem of time minimization in FL into two convex optimizations for short-term and long-term coherent timescales. The online SCA method effectively separates the nonconvex problem into two convex subproblems: optimizing local updating frequency, data rates, and beamforming vectors in the short-term, and focusing on learning accuracy in the long-term. Additionally, considering the energy constraints of edge nodes in 5G and beyond networks, we employ the simultaneous wireless information and power transfer (SWIPT) technique to support device energy needs. The power-splitting SWIPT (PS-SWIPT) method impacts the optimization problem primary constraints based on energy and power limits. Numerical results demonstrate that our proposed scheme reduces FL training time by up to 70.01% and 21.29%, while maintaining accuracy compared to cell-free time division multiple access massive MIMO (CF-TDMA mMIMO) and sensing-assisted sustainable federated learning (S2FL) schemes, respectively.
This paper explores the application of federated learning (FL) in 5G and 6G wireless networks, focusing on achieving a global model from user-local datasets. To enhance efficiency, we investigate minimizing training time within the collocated massive multiple-input multiple-output (CL-mMIMO) network framework. We propose an online successive convex approximation (SCA) approach to decompose the stochastic nonconvex problem of time minimization in FL into two convex optimizations for short-term and long-term coherent timescales. The online SCA method effectively separates the nonconvex problem into two convex subproblems: optimizing local updating frequency, data rates, and beamforming vectors in the short-term, and focusing on learning accuracy in the long-term. Additionally, considering the energy constraints of edge nodes in 5G and beyond networks, we employ the simultaneous wireless information and power transfer (SWIPT) technique to support device energy needs. The power-splitting SWIPT (PS-SWIPT) method impacts the optimization problem primary constraints based on energy and power limits. Numerical results demonstrate that our proposed scheme reduces FL training time by up to 70.01% and 21.29%, while maintaining accuracy compared to cell-free time division multiple access massive MIMO (CF-TDMA mMIMO) and sensing-assisted sustainable federated learning (S2FL) schemes, respectively.
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