Energy-Efficient Federated Learning With Resource Allocation for Green IoT Edge Intelligence in B5G
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...
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| Format: | Article |
| Language: | English |
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IEEE
2023
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| Online Access: | http://eprints.uthm.edu.my/8945/ http://eprints.uthm.edu.my/8945/1/J15916_cf78dab738eabff1c909c88fd9243b22.pdf |
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| author | SALH, ADEB NGAH, RAZALI Audah, Lukman SOON KIM, KWANG ALJALOUD, KHALED A. TALIB, HAIRUL NIZAM |
| author_facet | SALH, ADEB NGAH, RAZALI Audah, Lukman SOON KIM, KWANG ALJALOUD, KHALED A. TALIB, HAIRUL NIZAM |
| author_sort | SALH, ADEB |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T20:27:48Z |
| format | Article |
| id | uthm-8945 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:27:48Z |
| publishDate | 2023 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-89452023-06-18T01:36:45Z http://eprints.uthm.edu.my/8945/ Energy-Efficient Federated Learning With Resource Allocation for Green IoT Edge Intelligence in B5G SALH, ADEB NGAH, RAZALI Audah, Lukman SOON KIM, KWANG ALJALOUD, KHALED A. TALIB, HAIRUL NIZAM T Technology (General) 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. IEEE 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/8945/1/J15916_cf78dab738eabff1c909c88fd9243b22.pdf SALH, ADEB and NGAH, RAZALI and Audah, Lukman and SOON KIM, KWANG and ALJALOUD, KHALED A. and TALIB, HAIRUL NIZAM (2023) Energy-Efficient Federated Learning With Resource Allocation for Green IoT Edge Intelligence in B5G. Digital Object Identifier, 11. pp. 16353-16367. |
| spellingShingle | T Technology (General) SALH, ADEB NGAH, RAZALI Audah, Lukman SOON KIM, KWANG ALJALOUD, KHALED A. TALIB, HAIRUL NIZAM Energy-Efficient Federated Learning With Resource Allocation for Green IoT Edge Intelligence in B5G |
| title | Energy-Efficient Federated Learning With Resource Allocation for Green IoT Edge Intelligence in B5G |
| title_full | Energy-Efficient Federated Learning With Resource Allocation for Green IoT Edge Intelligence in B5G |
| title_fullStr | Energy-Efficient Federated Learning With Resource Allocation for Green IoT Edge Intelligence in B5G |
| title_full_unstemmed | Energy-Efficient Federated Learning With Resource Allocation for Green IoT Edge Intelligence in B5G |
| title_short | Energy-Efficient Federated Learning With Resource Allocation for Green IoT Edge Intelligence in B5G |
| title_sort | energy-efficient federated learning with resource allocation for green iot edge intelligence in b5g |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/8945/ http://eprints.uthm.edu.my/8945/1/J15916_cf78dab738eabff1c909c88fd9243b22.pdf |