# Lightwave Power Transfer for Federated Learning-based Wireless Networks

arXiv: https://arxiv.org/abs/2005.03977 · HTML: https://mlsanity.com/paper/2005.03977/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [UAV Communications for Sustainable Federated Learning](https://mlsanity.com/paper/2103.11073/) (2021)
- [Federated learning: Applications, challenges and future directions](https://mlsanity.com/paper/2205.09513/) (2022)
- [Fine-Grained Data Selection for Improved Energy Efficiency of Federated Edge Learning](https://mlsanity.com/paper/2106.12561/) (2021)
- [Energy Efficient Federated Learning in Integrated Fog-Cloud Computing Enabled Internet-of-Things Networks](https://mlsanity.com/paper/2107.03520/) (2021)
- [Threshold-Based Data Exclusion Approach for Energy-Efficient Federated Edge Learning](https://mlsanity.com/paper/2104.05509/) (2021)

## Abstract (arXiv)

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.