Energy-efficient federated learning with resource allocation for green IoT edge intelligence in B5G

Ngah, Razali and Salh, Adeeb and Audah, Lukman and Abdullah, Qazwan and Kim, Kwang Soon and Al-Moliki, Yahya Mohammed Hameed and AlJaloud, Khaled A. and Talib, Md Hairul Nizam (2023) Energy-efficient federated learning with resource allocation for green IoT edge intelligence in B5G. IEEE Access, 11. pp. 16353-16367. ISSN 2169-3536

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Abstract

An edge intelligence-aided Internet-of-Things (IoT) network has been proposed to accelerate the response of IoT services by deploying edge intelligence near IoT devices. The transmission of data from IoT devices to the edge nodes leads to large network traffic in the wireless connections. Federated Learning (FL) is proposed to solve the high computational complexity by training the model locally on IoT devices and sharing the model parameters in the edge nodes. This paper focuses on developing an efficient integration of joint edge intelligence nodes depending on investigating an energy-efficient bandwidth allocation, computing Central Processing Unit (CPU) frequency, optimization transmission power, and the desired level of learning accuracy to minimize the energy consumption and satisfy the FL time requirement for all IoT devices. The proposal efficiently optimized the computation frequency allocation and reduced energy consumption in IoT devices by solving the bandwidth optimization problem in closed form. The remaining computational frequency allocation, transmission power allocation, and loss could be resolved with an Alternative Direction Algorithm (ADA) to reduce energy consumption and complexity at every iteration of FL time from IoT devices to edge intelligence nodes. The simulation results indicated that the proposed ADA can adapt the central processing unit frequency and power transmission control to reduce energy consumption at the cost of a small growth of FL time.

Item Type:	Article
Uncontrolled Keywords:	Central processing unit, Edge nodes, Energy consumption, Federated learning, Internet-of-things
Divisions:	Faculty of Electrical Engineering
Depositing User:	Sabariah Ismail
Date Deposited:	04 Jul 2024 10:43
Last Modified:	04 Jul 2024 10:43
URI:	http://eprints.utem.edu.my/id/eprint/27324
Statistic Details:	View Download Statistic

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