Federated Learning for UAVs-Enabled Wireless Networks: Use Cases, Challenges, and Open Problems

Bouziane Brik,Adlen Ksentini,Maha Bouaziz

doi:10.1109/access.2020.2981430

Abstract

The use of Unmanned Aerial Vehicles (UAVs) for wireless networks is rapidly growing as key enablers of new applications, including: surveillance and monitoring, military, delivery of medical supplies, telecommunications, etc. In particular, due to their unique proprieties such as flexibility, mobility, and adaptive altitude, UAVs can act as mobile base stations to improve capacity, coverage, and energy efficiency of wireless networks. On the other hand, UAVs can operate as mobile terminals to enable many applications such as item delivery and real-time video streaming. In such context, data-driven Deep Learning-assisted (DL) approaches are gaining a growing interest to not only exploit the huge amount of generated data, but also to optimize the network operations, and hence ensure the QoS requirements of these emerging wireless networks. However, UAVs are resource-constrained devices especially in terms of computing and power resources, and traditional DL-assisted schemes are cloud-centric, which require UAVs’ data to be sent and stored in a centralized server. This represents a critical issue since it generates a huge network communication overhead to send raw data towards the centralized entity, and hence may lead to network bandwidth and energy inefficiency of UAV devices. In addition, the transferred data may contain personnel data such as UAVs’ localization and identity, which can directly affect UAVs’ privacy concerns. As a solution, Federated Deep Learning (FDL), or distributed DL, was introduced, where the basic idea is to keep raw data where it is generated, while sending only users’ local trained DL models to the centralized entity for aggregation. Due to its privacy-preserving and low communication overhead and latency, FDL is much more adequate for many UAVs-enabled wireless applications. In this work, we provide a general introduction of FDL application for UAV-enabled wireless networks. We first introduce the FDL concept and its fundamentals. Then, we highlight the possible applications of FDL in UAVs-enabled wireless networks by addressing the suitability and how to use FDL to deal with target challenges. Finally, we discuss about key technical challenges, open issues, and future research directions on FDL-based approaches in such context.

Highlights

Next-generation of wireless networks are undergoing a major revolution
We note that in this work, we focus more on Deep Neural Network (DNN) supervised learning, where there are several variants used according to the target problem [9], such as, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN)
This paper addressed the role of federated deep learning concept to deal with some challenges of Unmanned Aerial Vehicles (UAVs)-enabled wireless networks

Summary

INTRODUCTION

Next-generation of wireless networks are undergoing a major revolution. According to Cisco forecast, more than 75 billion of connected IoT devices are expected by 2025, ranging from sensors, wearable, smartphones, to connected cars, and Unmanned Aerial Vehicles (UAVs) [1]. The use of ML-based approaches for wireless networks is motivated by the huge amount of generated traffic data and the inefficiency of traditional model-based solutions that are not capable to deal with the dynamic complexity and heterogeneity of the wireless networks [8] This enables to integrate more intelligent functions in order to optimize the network operations and ensure, in real-time, different needs of emerging wireless applications. It was demonstrated that FDL is more suitable for ultra low latency applications since it enables wireless devices to collaboratively, and in parallel, learn a shared prediction model while keeping all the training data on device [12] This implies that FDL can be an enabling technology for UAVs-based wireless networks to train learning models, as compared to the centralized cloud-centric approaches.

OVERVIEW ON FEDERATED DEEP LEARNING

UAV FOR 5G CELLULAR NETWORKS UAV Applications

OPEN PROBLEMS AND FUTURE RESEARCH DIRECTIONS

CONCLUSION