Federated Learning with Blockchain Assisted Image Classification for Clustered UAV Networks

• Published in: Computers, materials & continua Vol. 72; no. 1; pp. 1195 - 1212
• Main Authors: Abunadi, Ibrahim, M. Althobaiti, Maha, N. Al-Wesabi, Fahd, Mustafa Hilal, Anwer, Medani, Mohammad, Ahmed Hamza, Manar, Rizwanullah, Mohammed, Serwar Zamani, Abu
• Format: Journal Article
• Language: English
• Published: Henderson Tech Science Press 2022
• Subjects: Algorithms; Big Data; Blockchain; Classification; Cloud computing; Clustering; Cryptography; Cyber-physical systems; Data processing; Data retrieval; Data transmission; Decision making; Federated learning; Image classification; Industrial applications; Internet of Things; Machine learning; Optimization; Remote sensing; Unmanned aerial vehicles
• ISSN: 1546-2226; 1546-2218; 1546-2226
• DOI: 10.32604/cmc.2022.025473

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.