Federated Learning with Blockchain Assisted Image Classification for Clustered UAV Networks

【Author】 Abunadi, Ibrahim; Althobaiti, Maha M.; Al-Wesabi, Fahd N.; Hilal, Anwer Mustafa; Medani, Mohammad; Hamza, Manar Ahmed; Rizwanullah, Mohammed; Zamani, Abu Serwar

【Source】CMC-COMPUTERS MATERIALS & CONTINUA

【Abstract】The evolving Industry 4.0 domain encompasses a collection of future industrial developments with cyber-physical systems (CPS), Internet of things (IoT), big data, cloud computing, etc. Besides, the industrial Internet of things (IIoT) directs data from systems for monitoring and controlling the physical world to the data processing system. A major novelty of the IIoT is the unmanned aerial vehicles (UAVs), which are treated as an efficient remote sensing technique to gather data from large regions. UAVs are commonly employed in the industrial sector to solve several issues and help decision making. But the strict regulations leading to data privacy possibly hinder data sharing across autonomous UAVs. Federated learning (FL) becomes a recent advancement of machine learning (ML) which aims to protect user data. In this aspect, this study designs federated learning with blockchain assisted image classification model for clustered UAV networks (FLBIC-CUAV) on IIoT environment. The proposed FLBIC-CUAV technique involves three major processes namely clustering, blockchain enabled secure communication and FL based image classification. For UAV cluster construction process, beetle swarm optimization (BSO) algorithm with three input parameters is designed to cluster the UAVs for effective communication. In addition, blockchain enabled secure data transmission process take place to transmit the data from UAVs to cloud servers. Finally, the cloud server uses an FL with Residual Network model to carry out the image classification process. A wide range of simulation analyses takes place for ensuring the betterment of the FLBIC-CUAV approach. The experimental outcomes portrayed the betterment of the FLBIC-CUAV approach over the recent state of art methods.

【Keywords】UAV networks; clustering; secure communication; blockchain; federated learning; image classification

【标题】用于集群无人机网络的区块链辅助图像分类联邦学习

【摘要】不断发展的工业 4.0 领域涵盖了未来工业发展的集合，包括信息物理系统 (CPS)、物联网 (IoT)、大数据、云计算等。此外，工业物联网 (IIoT) 引导来自系统的数据用于监视和控制物理世界到数据处理系统。 IIoT 的一个主要新颖之处是无人驾驶飞行器 (UAV)，它被视为一种从大区域收集数据的有效遥感技术。无人机通常用于工业部门，以解决多个问题并帮助决策。但导致数据隐私的严格规定可能会阻碍自主无人机之间的数据共享。联邦学习 (FL) 成为机器学习 (ML) 的最新进展，旨在保护用户数据。在这方面，本研究为 IIoT 环境下的集群无人机网络 (FLBIC-CUAV) 设计了具有区块链辅助图像分类模型的联邦学习。所提出的 FLBIC-CUAV 技术涉及三个主要过程，即聚类、启用区块链的安全通信和基于 FL 的图像分类。对于无人机集群构建过程，设计了具有三个输入参数的甲虫群优化（BSO）算法对无人机进行聚类以实现有效通信。此外，启用区块链的安全数据传输过程将数据从无人机传输到云服务器。最后，云服务器使用带有残差网络的 FL 模型来执行图像分类过程。为了确保 FLBIC-CUAV 方法的改进，进行了广泛的模拟分析。实验结果描绘了 FLBIC-CUAV 方法相对于最近最先进的方法的改进。

【关键词】无人机网络；聚类；安全通信；区块链；联邦学习；图像分类

【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】3.860

【翻译者】石东瑛