Blockchain-Based Federated Learning in UAVs Beyond 5G Networks: A Solution Taxonomy and Future Directions

【Author】 Saraswat, Deepti; Verma, Ashwin; Bhattacharya, Pronaya; Tanwar, Sudeep; Sharma, Gulshan; Bokoro, Pitshou N.; Sharma, Ravi

【Source】IEEE ACCESS

【Abstract】Recently, unmanned aerial vehicles (UAVs) have gained attention due to increased use-cases in healthcare, monitoring, surveillance, and logistics operations. UAVs mainly communicate with mobile base stations, ground stations (GS), or networked peer UAVs, known as UAV swarms. UAVs communicate with GS, or UAV swarms, over wireless channels to support mission-critical operations. Communication latency, bandwidth, and precision are of prime importance in such operations. With the rise of data-driven applications, fifth-generation (5G) networks would face bottlenecks to communicate at near-real-time, at low latency and improved coverage. Thus, researchers have shifted towards network designs that incorporate beyond 5G (B5G) networks for UAV designs. However, UAVs are resource-constrained, with limited power and battery, and thus centralized cloud-centric models are not suitable. Moreover, as exchanged data is through open channels, privacy and security issues exist. Federated learning (FL) allows data to be trained on local nodes, preserving privacy and improving network communication. However, sharing of local updates is required through a trusted consensus mechanism. Thus, blockchain (BC)-based FL schemes for UAVs allow trusted exchange of FL updates among UAV swarms and GS. To date, limited research has been carried out on the integration of BC and FL in UAV management. The proposed survey addresses the gap and presents a solution taxonomy of BC-based FL in UAVs for B5G networks due to the open problem. This paper presents a reference architecture and compares its potential benefits over traditional BC-based UAV networks. Open issues and challenges are discussed, with possible future directions. Finally, a logistics case study of BC-based FL-oriented UAVs in 6G networks is presented. The survey aims to aid researchers in developing potential UAV solutions with the key integrating principles over a diverse set of application verticals.

【Keywords】5G mobile communication; Autonomous aerial vehicles; Servers; Security; Data privacy; Data models; Computational modeling; Beyond 5G networks; 6G; blockchain; federated learning; unmanned aerial vehicles

【标题】超越 5G 网络的无人机中基于区块链的联邦学习：解决方案分类和未来方向

【摘要】最近，由于在医疗保健、监控、监视和物流操作中的用例增加，无人驾驶飞行器 (UAV) 受到了关注。无人机主要与移动基站、地面站 (GS) 或联网的对等无人机进行通信，称为无人机群。无人机通过无线信道与 GS 或无人机群通信，以支持关键任务操作。通信延迟、带宽和精度在此类操作中至关重要。随着数据驱动应用程序的兴起，第五代 (5G) 网络将面临接近实时、低延迟和改善覆盖范围的通信瓶颈。因此，研究人员已转向将 5G (B5G) 网络以外的网络设计纳入无人机设计。然而，无人机资源受限，功率和电池有限，因此集中式以云为中心的模型不适合。此外，由于交换数据是通过开放渠道进行的，因此存在隐私和安全问题。联邦学习 (FL) 允许在本地节点上训练数据，保护隐私并改善网络通信。但是，需要通过可信的共识机制共享本地更新。因此，基于区块链 (BC) 的无人机 FL 方案允许在无人机群和 GS 之间进行可信的 FL 更新交换。迄今为止，关于在无人机管理中集成 BC 和 FL 的研究有限。由于开放问题，拟议的调查解决了这一差距，并提出了 B5G 网络无人机中基于 BC 的 FL 的解决方案分类。本文介绍了一种参考架构，并比较了其与传统的基于 BC 的无人机网络相比的潜在优势。讨论了未解决的问题和挑战，以及可能的未来方向。最后，介绍了 6G 网络中基于 BC 的面向 FL 的无人机的物流案例研究。该调查旨在帮助研究人员开发具有关键集成原则的潜在无人机解决方案，涵盖各种应用垂直领域。

【关键词】5G移动通信；自主飞行器；服务器；安全;数据隐私；数据模型；计算建模；超越 5G 网络； 6G；区块链；联邦学习；无人机

【发表时间】2022

【收录时间】2022-07-06

【文献类型】Article

【论文大主题】区块链联邦学习

【论文小主题】联邦学习为主体

【影响因子】3.476

【翻译者】石东瑛