Federated Learning for UAVs-Enabled Wireless Networks: Use Cases, Challenges, and Open Problems
Federated Learning for UAVs-Enabled Wireless Networks: Use Cases, Challenges, and Open Problems
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DOI：
10.1109/access.2020.2981430
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Bouaziz, Maha
中科院分区：
文献类型：
--
作者：
Brik, Bouziane;Ksentini, Adlen;Bouaziz, Maha
无人机（UAV）在无线网络中的使用正在迅速增长，成为新应用的关键推动者，包括：监视和监测、军事、医疗用品的递送、电信等。特别是，由于其独特的特性，如灵活性、移动性和自适应高度，UAV可以充当移动的基站，以提高无线网络的容量、覆盖范围和能效。另一方面，UAV可以作为移动的终端来操作，以实现许多应用，例如物品递送和实时视频流。在这种情况下，数据驱动的深度学习辅助（DL）方法越来越受到关注，不仅可以利用生成的大量数据，还可以优化网络操作，从而确保这些新兴无线网络的QoS要求。然而，UAV是资源受限的设备，特别是在计算和电力资源方面，并且传统的DL辅助方案是以云为中心的，这要求UAV的数据被发送并存储在集中式服务器中。这代表了一个关键问题，因为它产生了巨大的网络通信开销来向集中式实体发送原始数据，因此可能导致UAV设备的网络带宽和能量效率低下。此外，传输的数据可能包含个人数据，如无人机的定位和身份，这可能直接影响无人机的隐私问题。作为一种解决方案，引入了联合深度学习（FDL）或分布式DL，其基本思想是将原始数据保存在生成的位置，同时仅将用户的本地训练的DL模型发送到集中式实体进行聚合。由于其隐私保护和低通信开销和延迟，FDL更适合许多支持UAV的无线应用。在这项工作中，我们提供了一个通用的介绍FDL应用于无人机使能的无线网络。我们首先介绍FDL的概念及其基本原理。然后，我们强调FDL在无人机使能的无线网络中的可能应用，解决的适用性和如何使用FDL来处理目标的挑战。最后，我们讨论了在这种背景下，基于FDL的方法的关键技术挑战，开放问题和未来的研究方向。
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.