Lightwave Power Transfer for Federated Learning-Based Wireless Networks
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.
IEEE Communications Letters
Digital Object Identifier (DOI)
H. Tran, G. Kaddoum, H. Elgala, C. Abou-Rjeily and H. Kaushal, "Lightwave Power Transfer for Federated Learning-Based Wireless Networks," in IEEE Communications Letters, vol. 24, no. 7, pp. 1472-1476, July 2020, doi: 10.1109/LCOMM.2020.2985698.