Can I read Lightwave Power Transfer for Federated Learning-based Wireless Networks on EtoBox?

Lightwave Power Transfer for Federated Learning-based Wireless Networks by Tran, Ha-Vu; Kaddoum, Georges; Elgala, Hany; Abou-Rjeily, Chadi; Kaushal, Hemani is a scholarly article available to read on EtoBox.

What is Lightwave Power Transfer for Federated Learning-based Wireless Networks about?

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.

Author

Tran, Ha-Vu; Kaddoum, Georges; Elgala, Hany; Abou-Rjeily, Chadi; Kaushal, Hemani

Published

2020

Language

EN