Lightwave Power Transfer for Federated Learning-based Wireless Networks

• URL: http://arxiv.org/abs/2005.03977v1
• Date: Sat, 11 Apr 2020 16:27:17 GMT
• Title: Lightwave Power Transfer for Federated Learning-based Wireless Networks
• Authors: Ha-Vu Tran, Georges Kaddoum, Hany Elgala, Chadi Abou-Rjeily and Hemani Kaushal
• Abstract summary: Federated Learning (FL) has been recently presented as a new technique for training shared machine learning models in a distributed manner. implementing FL in wireless networks may significantly reduce the lifetime of energy-constrained mobile devices. We propose a novel approach at the physical layer based on the application of lightwave power transfer in the FL-based wireless network.
• Score: 34.434349833489954
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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