Overview

The convergence of blockchain, the Internet of Everything (IoE), and federated learning paves the way for enhanced security in digital ecosystems. Blockchain offers decentralized, tamper-proof solutions that ensure data integrity, while the IoE connects smart devices, generating large amounts of data that require robust protection. Federated learning allows models to be trained locally on edge devices without transferring sensitive data to centralized servers, minimizing exposure to cyber threats. These technologies strengthen privacy and data security while enabling more efficient, scalable, and resilient systems. Further research into the potential of these technologies may redefine how security is managed, ensuring a safer environment for individuals and organizations. Convergence of Blockchain, Internet of Everything, and Federated Learning for Security explores the convergence of blockchain, IoEs, federated learning, and cybersecurity, highlighting their relevance in the modern digital landscape. It examines the importance of these technologies in addressing security challenges and enhancing data privacy in interconnected systems. This book covers topics such as cryptography, machine learning, and smart grids, and is a useful resource for business owners, computer engineers, data scientists, academicians, and researchers.

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Fasee Ullah graduated from Universiti Teknologi Malaysia (UTM), Malaysia, Faculty of computing in 2017. Dr. Fasee has also completed the postdoctoral fellowship from the University of Macau (2019-2021), which is the academic talented program of the Government of Macau. Currently, he is working as Associate Professor at the department of Computer & Information Sciences, Universiti Teknologi PETRONAS, Perak, Malaysia. He has published many research papers in reputed impact factor journals and conferences. Dr. Fasee is the recipient of the Chancellor Award and the Best Student Award at UTM during his PhD, for his excellent research contributions to wireless communication and health monitoring. His research areas included are Wireless Body Area Network, Wireless Sensor Networks, Cloud Security, Smart hash security designing, smart cities, Big data analytics, Machine learning, deep learning and internet of things. He is currently providing reviewing services to IEEE Transactions on computers, Transactions on Network Science and Engineering, IEEE Transactions on Cloud Computing, IEEE Access, IEEE Sensor Journal, ACM, and International Journal of Distributed Sensor Networks. Arfat Ahmad Khan received the B.Eng. de- gree in electrical engineering from the University of Lahore, Pakistan, in 2013, the M.Eng. degree in electrical engineering from the Government College University Lahore, Pakistan, in 2015, and the Ph.D. degree in telecommunication and com- puter engineering from the Suranaree University of Technology, Thailand, in 2018. From 2014 to 2016, he was an RF Engineer with Etisalat, United Arab Emirates. From 2018 to 2022, he worked as a Lecturer and a Senior Researcher with the Suranaree University of Technology. He is currently working as a Senior Lecturer and a Researcher at Khon Kaen University, Thailand. He has published more than 50 high impact factored research articles in reputed journals, such as IEEE transactions, Elsevier, etc. His research interests include optimization and stochastic pro- cesses, wireless sensor networks, Machine, deep and federated learning for intelligent systems, such as Agriculture, Internet Security, Image processing, etc., and the advance wireless communications. Asad Ullah Dedicated and accomplished computer science professional with expertise in Artificial Intelligence. Adept at conducting cutting-edge research, delivering engaging lectures, and mentoring students to foster their academic and professional growth. Committed to advancing the field of computer science through innovative research contributions and educational excellence. Proven leadership abilities in academic administration and collaborative research initiatives.

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