Title

Lightwave Power Transfer for Federated Learning-Based Wireless Networks

Document Type

Article

Publication Date

7-1-2020

Subject Area

ARRAY(0x557372e4f880)

Abstract

Federated Learning (FL) has been recently presented as a new technique for training shared machine learning models in a distributed manner while respecting data privacy. However, implementing FL in wireless networks may significantly reduce the lifetime of energy-constrained mobile devices due to their involvement in the construction of the shared learning models. To handle this issue, we propose a novel approach at the physical layer based on the application of lightwave power transfer in the FL-based wireless network and a resource allocation scheme to manage the network's power efficiency. Hence, we formulate the corresponding optimization problem and then propose a method to obtain the optimal solution. Numerical results reveal that, the proposed scheme can provide sufficient energy to a mobile device for performing FL tasks without using any power from its own battery. Hence, the proposed approach can support the FL-based wireless network to overcome the issue of limited energy in mobile devices.

Publication Title

IEEE Communications Letters

Volume

24

Issue

7

First Page

1472

Last Page

1476

Digital Object Identifier (DOI)

10.1109/LCOMM.2020.2985698

ISSN

10897798

E-ISSN

15582558

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