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Energy-Efficient Wireless Power Transfer for Sustainable Federated Learning

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Abstract

Federated Learning (FL) has attracted great attention in recent years and is considered as an enabling technology in future smart wireless networks. Nevertheless, this learning paradigm faces a severe challenge in its implementation procedure, i.e., energy shortage issue. Different from the traditional centralized training paradigm, the training procedure of FL is carried out on mobile devices. Generally, the training tasks are computation-intensive and may involve several communication rounds for transmitting large-sized machine learning models, which indicates that they are high energy-consuming. This characteristic increases burden on mobile devices with limited battery capacity. In this paper, we employ the Radio Frequency (RF)-based Wireless Power Transfer (WPT) technology and time switching energy harvesting architecture to realize a sustainable FL framework, and then design a resource optimization strategy based on the dual and line search methods to minimize the amount of Transferred Energy (TE) required for completing the learning. Moreover, we interpret the Karush–Kuhn–Tucker (KKT) conditions of the formulated problem and obtain some engineering insights. Simulation results verify the convergence of the proposed resource optimization strategy and demonstrate the advantage of the proposed framework over the existing work in terms of TE.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Notes

  1. This assumption is practical. Due to the size and cost limitation, many mobile devices are only equipped with one antenna. This assumption also exists in many papers, such as [7,8,9].

  2. Note that C3 contains N constraints corresponding to N devices.

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Funding

This work is financially supported by Shenzhen Science and Technology Program under Grant No. JCYJ2021032413240-6016 and Anhui Provincial Natural Science Foundation No.2008085MF208.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China

    Youqiang Hu & Hejiao Huang

  2. School of Electrical Engineering, Anhui Polytechnic University, Wuhu, China

    Nuo Yu

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  1. Youqiang Hu
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  2. Hejiao Huang
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  3. Nuo Yu
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Contributions

All authors contributed to the study conception and design. YH performed material preparation, modelled the system, developed algorithms and completed simulations. HH reviewed the manuscript and provided suggestions. NY also reviewed the manuscript and provided suggestions. All authors read and approved the final manuscript.

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Correspondence to Hejiao Huang.

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Hu, Y., Huang, H. & Yu, N. Energy-Efficient Wireless Power Transfer for Sustainable Federated Learning. Wireless Pers Commun 134, 831–855 (2024). https://doi.org/10.1007/s11277-024-10929-3

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