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

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. INDEX TERMS Deep learning, federated deep learning, UAVs-based wireless networks, wireless communications.

A Classification of the Enabling Techniques for Low Latency and Reliable Communications in 5G and Beyond: AI-Enabled Edge Caching

“…However, [28], [38] failed to discuss the privacy preserving mechanisms of the FL edge caching frameworks. Various issues of FL schemes and FL use case scenarios were considered in [39], [40] while [41] discussed the state-ofthe-art strategies of machine learning techniques for edge caching. However, [39], [41], [42] failed to present performance analysis of the state-of-the-art edge caching schemes.…”

“…Various issues of FL schemes and FL use case scenarios were considered in [39], [40] while [41] discussed the state-ofthe-art strategies of machine learning techniques for edge caching. However, [39], [41], [42] failed to present performance analysis of the state-of-the-art edge caching schemes. On the other hand, [43] compared the performance of the FL and centralized schemes.…”

“…On the other hand, it is important to achieve bounded ULL in applications such as autonomous automotive vehicles and industrial automation and control systems. Nevertheless, the studies in [1], [3], [4], [9], [28], [34]- [39], [41], [43] failed to discuss the enabling techniques for achieving bounded ULL. In contrast, bounded ULL was considered in [16], [31], [44].…”

“…In [24], [37], [39], [41], [87]- [90], it was discussed that FL is a promising technique for future intelligent networks due to its superior performance features and added benefits. Edge caching solutions which are based on FL algorithms can guarantee smart models, reduced content delivery latency, improved content acquisition reliability, and improved energy efficiency, all while ensuring preservation of personal data privacy and security.…”

“… DL can extract new features automatically for different problems [130]. Thus, DL enables the integration of more intelligent functions in order to optimize the network operations and ensure in real-time, different needs of the emerging wireless applications [39]. With DL, devices are able to intelligently control their environment as well as proactively taking more adequate actions by learning and predicting the dynamic evolution of various network features such as the traffic pattern, content requests, mobility distribution, communication channel dynamics, and user context.…”