UAV Communications for Sustainable Federated Learning

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.

I. Introduction

To overcome challenges of data privacy in conventional deep learning, the concept of federated learning (FL) has been invented by Google in 2016 [1]. In FL, users need not share their data with the central server but only information of the local models is transmitted to the server. Thanks to its characteristics, FL has been adopted in many applications such as Google keyboard suggestions [2], medicine [3], and mobile communications [4]. Although FL has found many benefits in enabling privacy-preserving learning solutions, it also poses significant challenges such as resource management, robustness, and incentive mechanisms [5]. Moreover, powering mobile users with limited battery capacity is of vital importance to enable FL as users may run out of battery during the training process. In this regard, wireless powered communication networks (WPCNs) represent a promising solution, owing to their ability to charge the users wirelessly. Nonetheless, conventional WPCNs often suffer from several challenges, such as doubly near-far issue (i.e., far users need to spend higher power with smaller harvested energy) and severe performance degradation over long distance [6]. To address these challenges, unmanned aerial vehicle (UAV) enabled WPCNs have been introduced, which often supports line-of-sight connections with flexible deployment and controllable mobility [6], thus improving the network performance.