UAV Communications for Sustainable Federated Learning

TL;DR

This paper proposes a UAV-powered wireless power transfer system to enable sustainable federated learning networks, optimizing UAV operations to reduce power consumption and improve efficiency.

Contribution

It introduces UAV-SFL, an innovative algorithm that jointly optimizes UAV placement, power, and bandwidth to enhance sustainable federated learning in wireless networks.

Findings

UAV-SFL reduces UAV transmit power by up to 78.81%.

The approach improves power efficiency and sustainability of FL networks.

Simulation results validate the effectiveness of the proposed method.

Abstract

Federated learning (FL), invented by Google in 2016, has become a hot research trend. However, enabling FL in wireless networks has to overcome the limited battery challenge of mobile users. In this regard, we propose to apply unmanned aerial vehicle (UAV)-empowered wireless power transfer to enable sustainable FL-based wireless networks. The objective is to maximize the UAV transmit power efficiency, via a joint optimization of transmission time and bandwidth allocation, power control, and the UAV placement. Directly solving the formulated problem is challenging, due to the coupling of variables. Hence, we leverage the decomposition technique and a successive convex approximation approach to develop an efficient algorithm, namely UAV for sustainable FL (UAV-SFL). Finally, simulations illustrate the potential of our proposed UAV-SFL approach in providing a sustainable solution for FL-based wireless networks, and in reducing the UAV transmit power by 32.95%, 63.18%, and 78.81% compared with the benchmarks.