User Scheduling in Federated Learning over Energy Harvesting Wireless Networks

Rami Hamdi; Mingzhe Chen; Ahmed Ben Said; Marwa Qaraqe; H. Vincent Poor

doi:10.1109/GLOBECOM46510.2021.9685801

User Scheduling in Federated Learning over Energy Harvesting Wireless Networks

Rami Hamdi, Mingzhe Chen, Ahmed Ben Said, Marwa Qaraqe, H. Vincent Poor

Engineering

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

5 Citations (Scopus)

Abstract

In this paper, the deployment of federated learning (FL) is investigated in an energy harvesting wireless network in which the base station (BS) is equipped with a massive multiple-input multiple-output (MIMO) system and a set of users powered by independent energy harvesting sources to cooperatively perform FL. Since a certain number of users may not be served due to interference and energy constraints, a joint energy management and user scheduling problem is considered. This problem is formulated as an optimization problem whose goal is to minimize the FL training loss via optimizing user scheduling. To determine the effect of various wireless factors (transmit power and number of scheduled users) on training loss, the convergence rate of the FL algorithm is analyzed. Given this analytical result, the original user scheduling and energy management optimization problem can be decomposed, simplified and solved. Simulation results show that the proposed algorithm can reduce training loss compared to a standard FL algorithm.

Original language	English
Title of host publication	2021 IEEE Global Communications Conference (GLOBECOM 2021)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Number of pages	6
ISBN (Electronic)	9781728181042
DOIs	https://doi.org/10.1109/GLOBECOM46510.2021.9685801
Publication status	Published - 2 Feb 2022
Event	2021 IEEE Global Communications Conference, GLOBECOM 2021 - Madrid, Spain Duration: 7 Dec 2021 → 11 Dec 2021

Conference

Conference	2021 IEEE Global Communications Conference, GLOBECOM 2021
Country/Territory	Spain
City	Madrid
Period	7/12/21 → 11/12/21

Bibliographical note

Funding Information:
This research was sponsored in part by the TÜB˙ITAK—QNRF Joint Funding Program grant (AICC03-0324-200005) from the Scientific and Technological Research Council of Turkey and Qatar National Research Fund (QNRF) and in part by the U.S. National Science Foundation under Grant CCF-1908308. The findings achieved herein are solely the responsibility of the authors.

Keywords

energy harvesting
federated learning
resource allocation

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10.1109/GLOBECOM46510.2021.9685801Licence: Unspecified

Cite this

Hamdi, R, Chen, M, Ben Said, A, Qaraqe, M & Poor, HV 2022, User Scheduling in Federated Learning over Energy Harvesting Wireless Networks. in 2021 IEEE Global Communications Conference (GLOBECOM 2021). Institute of Electrical and Electronics Engineers Inc., 2021 IEEE Global Communications Conference, GLOBECOM 2021, Madrid, Spain, 7/12/21. https://doi.org/10.1109/GLOBECOM46510.2021.9685801

@inproceedings{550c2ad0d6e74a929378562a26a5e82e,

title = "User Scheduling in Federated Learning over Energy Harvesting Wireless Networks",

abstract = "In this paper, the deployment of federated learning (FL) is investigated in an energy harvesting wireless network in which the base station (BS) is equipped with a massive multiple-input multiple-output (MIMO) system and a set of users powered by independent energy harvesting sources to cooperatively perform FL. Since a certain number of users may not be served due to interference and energy constraints, a joint energy management and user scheduling problem is considered. This problem is formulated as an optimization problem whose goal is to minimize the FL training loss via optimizing user scheduling. To determine the effect of various wireless factors (transmit power and number of scheduled users) on training loss, the convergence rate of the FL algorithm is analyzed. Given this analytical result, the original user scheduling and energy management optimization problem can be decomposed, simplified and solved. Simulation results show that the proposed algorithm can reduce training loss compared to a standard FL algorithm.",

keywords = "energy harvesting, federated learning, resource allocation",

author = "Rami Hamdi and Mingzhe Chen and {Ben Said}, Ahmed and Marwa Qaraqe and Poor, {H. Vincent}",

note = "Funding Information: This research was sponsored in part by the T{\"U}B˙ITAK—QNRF Joint Funding Program grant (AICC03-0324-200005) from the Scientific and Technological Research Council of Turkey and Qatar National Research Fund (QNRF) and in part by the U.S. National Science Foundation under Grant CCF-1908308. The findings achieved herein are solely the responsibility of the authors. ; 2021 IEEE Global Communications Conference, GLOBECOM 2021 ; Conference date: 07-12-2021 Through 11-12-2021",

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doi = "10.1109/GLOBECOM46510.2021.9685801",

language = "English",

booktitle = "2021 IEEE Global Communications Conference (GLOBECOM 2021)",

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AU - Hamdi, Rami

AU - Chen, Mingzhe

AU - Ben Said, Ahmed

AU - Qaraqe, Marwa

AU - Poor, H. Vincent

N1 - Funding Information: This research was sponsored in part by the TÜB˙ITAK—QNRF Joint Funding Program grant (AICC03-0324-200005) from the Scientific and Technological Research Council of Turkey and Qatar National Research Fund (QNRF) and in part by the U.S. National Science Foundation under Grant CCF-1908308. The findings achieved herein are solely the responsibility of the authors.

PY - 2022/2/2

Y1 - 2022/2/2

N2 - In this paper, the deployment of federated learning (FL) is investigated in an energy harvesting wireless network in which the base station (BS) is equipped with a massive multiple-input multiple-output (MIMO) system and a set of users powered by independent energy harvesting sources to cooperatively perform FL. Since a certain number of users may not be served due to interference and energy constraints, a joint energy management and user scheduling problem is considered. This problem is formulated as an optimization problem whose goal is to minimize the FL training loss via optimizing user scheduling. To determine the effect of various wireless factors (transmit power and number of scheduled users) on training loss, the convergence rate of the FL algorithm is analyzed. Given this analytical result, the original user scheduling and energy management optimization problem can be decomposed, simplified and solved. Simulation results show that the proposed algorithm can reduce training loss compared to a standard FL algorithm.

AB - In this paper, the deployment of federated learning (FL) is investigated in an energy harvesting wireless network in which the base station (BS) is equipped with a massive multiple-input multiple-output (MIMO) system and a set of users powered by independent energy harvesting sources to cooperatively perform FL. Since a certain number of users may not be served due to interference and energy constraints, a joint energy management and user scheduling problem is considered. This problem is formulated as an optimization problem whose goal is to minimize the FL training loss via optimizing user scheduling. To determine the effect of various wireless factors (transmit power and number of scheduled users) on training loss, the convergence rate of the FL algorithm is analyzed. Given this analytical result, the original user scheduling and energy management optimization problem can be decomposed, simplified and solved. Simulation results show that the proposed algorithm can reduce training loss compared to a standard FL algorithm.

KW - energy harvesting

KW - federated learning

KW - resource allocation

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U2 - 10.1109/GLOBECOM46510.2021.9685801

DO - 10.1109/GLOBECOM46510.2021.9685801

M3 - Published conference contribution

AN - SCOPUS:85109962034

BT - 2021 IEEE Global Communications Conference (GLOBECOM 2021)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE Global Communications Conference, GLOBECOM 2021

Y2 - 7 December 2021 through 11 December 2021

ER -

User Scheduling in Federated Learning over Energy Harvesting Wireless Networks

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Keywords

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