中文版 | English
Title

Joint Bandwidth Allocation, Computation Control, and Device Scheduling for Federated Learning with Energy Harvesting Devices

Author
DOI
Publication Years
2022-10-31
Conference Name
56th Asilomar Conference on Signals, Systems, and Computers
ISSN
1058-6393
ISBN
978-1-6654-5907-5
Source Title
Pages
1164-1168
Conference Date
31 Oct.-2 Nov. 2022
Conference Place
Pacific Grove, CA, USA
Publication Place
345 E 47TH ST, NEW YORK, NY 10017 USA
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
Language
English
URL[Source Record]
Indexed By
WOS Research Area
Computer Science ; Engineering ; Telecommunications
WOS Subject
Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Engineering, Electrical & Electronic ; Telecommunications
WOS Accession No
WOS:000976687600214
Data Source
IEEE
PDF urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10051949
Citation statistics
Cited Times [WOS]:0
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/502102
DepartmentDepartment 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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