Lightwave Power Transfer for Federated Learning-based Wireless Networks
This addresses the energy limitation issue for mobile devices in federated learning networks, representing an incremental improvement by applying existing power transfer technology to a new context.
The paper tackles the energy consumption problem of mobile devices in federated learning-based wireless networks by proposing a lightwave power transfer approach and resource allocation scheme, achieving the ability to power devices for FL tasks without using their own battery.
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.