Resource Optimization for Wireless Federated Learning

Choong Seon Hong; Latif U. Khan; Mingzhe Chen; Dawei Chen; Walid Saad; Zhu Han

doi:10.1007/978-981-16-4963-9_3

Resource Optimization for Wireless Federated Learning

Choong Seon Hong, Latif U. Khan, Mingzhe Chen, Dawei Chen, Walid Saad, Zhu Han

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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Abstract

This chapter provides an overview of various resources such as computational, communication, and power resources, required for wireless federated learning. We perform convergence analysis of wireless federated learning. Additionally, joint resource and power allocation for wireless federated learning are proposed. Finally, we present a collaborative federated learning framework to efficiently enable the participation of communication-resource deficient devices in the federated learning process for performance improvement.

Original language	English
Title of host publication	Wireless Networks (United Kingdom)
Publisher	Springer Science and Business Media B.V.
Pages	27-69
Number of pages	43
DOIs	https://doi.org/10.1007/978-981-16-4963-9_3
Publication status	Published - 2021

Publication series

Name	Wireless Networks (United Kingdom)
ISSN (Print)	2366-1186
ISSN (Electronic)	2366-1445

Bibliographical note

Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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10.1007/978-981-16-4963-9_3

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abstract = "This chapter provides an overview of various resources such as computational, communication, and power resources, required for wireless federated learning. We perform convergence analysis of wireless federated learning. Additionally, joint resource and power allocation for wireless federated learning are proposed. Finally, we present a collaborative federated learning framework to efficiently enable the participation of communication-resource deficient devices in the federated learning process for performance improvement.",

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Resource Optimization for Wireless Federated Learning

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