Title | Joint Bandwidth Allocation, Computation Control, and Device Scheduling for Federated Learning with Energy Harvesting Devices |
Author | |
DOI | |
Publication Years | 2022-10-31
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Conference Name | 56th Asilomar Conference on Signals, Systems, and Computers
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ISSN | 1058-6393
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ISBN | 978-1-6654-5907-5
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Source Title | |
Pages | 1164-1168
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Conference Date | 31 Oct.-2 Nov. 2022
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Conference Place | Pacific Grove, CA, USA
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Publication Place | 345 E 47TH ST, NEW YORK, NY 10017 USA
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Publisher | |
Abstract | Federated learning (FL) is a promising technique for distilling artificial intelligence from massive data distributed in Internet-of-Things (IoT) networks while keeping their privacy. However, the efficient deployment of FL faces several challenges due to e.g., limited radio resources, computation capability, and battery capacity of IoT devices. To address these challenges, in this work, the energy harvesting technique is first enabled on IoT devices for supporting their long-term training. Then, the convergence rate of the FL algorithm is derived, which indicates that for reducing the learning latency, the data utility, i.e., the number of training samples, should be maximized in each training iteration. To this end, a data utility maximization problem for each iteration is formulated, under the constraints of limited time, bandwidth, computation frequency, and energy supply. The problem is mixed-integer and non-convex, and hence NP-hard. A joint bandwidth allocation, computation control, and device selection scheme is proposed. In the scheme, an energy-efficient training data contribution indicator is first derived for each device, and then a sequential device scheduling scheme is designed. |
Keywords | |
SUSTech Authorship | Others
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Language | English
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URL | [Source Record] |
Indexed By | |
WOS Research Area | Computer Science
; Engineering
; Telecommunications
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WOS Subject | Computer Science, Artificial Intelligence
; Computer Science, Interdisciplinary Applications
; Engineering, Electrical & Electronic
; Telecommunications
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WOS Accession No | WOS:000976687600214
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Data Source | IEEE
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PDF url | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10051949 |
Citation statistics |
Cited Times [WOS]:0
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Document Type | Conference paper |
Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/502102 |
Department | Department of Electrical and Electronic Engineering |
Affiliation | 1.School of Information Science and Technology, ShanghaiTech University, Shanghai, China 2.Shenzhen Research Institute of Big Data, Shenzhen, China 3.Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China 4.School of Electronic Information, Wuhan University, Wuhan, China |
Recommended Citation GB/T 7714 |
Li Zeng,Dingzhu Wen,Guangxu Zhu,et al. Joint Bandwidth Allocation, Computation Control, and Device Scheduling for Federated Learning with Energy Harvesting Devices[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2022:1164-1168.
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