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Demystifying Federated Learning for Blockchain and Industrial Internet of Things
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Demystifying Federated Learning for Blockchain and Industrial Internet of ThingsНазвание: Demystifying Federated Learning for Blockchain and Industrial Internet of Things
Автор: Sandeep Kautish, Gaurav Dhiman
Издательство: IGI Global
Год: 2022
Страниц: 240
Язык: английский
Формат: epub (true)
Размер: 17.2 MB

In recent years, mobile technology and the internet of objects have been used in mobile networks to meet new technical demands. Emerging needs have centered on data storage, computation, and low latency management in potentially smart cities, transport, smart grids, and a wide number of sustainable environments. Federated learning's contributions include an effective framework to improve network security in heterogeneous industrial internet of things (IIoT) environments. Demystifying Federated Learning for Blockchain and Industrial Internet of Things rediscovers, redefines, and reestablishes the most recent applications of Federated Learning (FL) using blockchain and IIoT to optimize data for next-generation networks. It provides insights to readers in a way of inculcating the theme that shapes the next generation of secure communication. Covering topics such as smart agriculture, object identification, and educational big data, this premier reference source is an essential resource for computer scientists, programmers, government officials, business leaders and managers, students and faculty of higher education, researchers, and academicians.

Industrial Internet is an important keystone of the Industry 4.0 and a key element to transform old kinetic energy into a new era. With the help of latest development of IIoT, a single form of equipment and different types of enterprises have been connected by the Industrial Internet, which allows the resources of different links to be organically combined. The Industrial Internet system architecture consists of four aspects: network connection, platform, security system, and identification analysis system. Among them, the network is used to realize the connection of people, machines, and things, and it is the foundation of Industrial Internet. The security system is responsible for providing security protection and guarantee, and the purpose of the platform is to open up operational data and Internet data to integrate resources.

Mobile technology and the internet of objects have been used in mobile networks to meet new technical demands. Emerging needs have centered on data storage, computation, and low latency management in potentially smart cities, transport, smart grids, and a wide number of sustainable environments. Federated learning’s contributions include an effective framework to improve network security in heterogeneous industrial internet of things (IIoT) environments.

Federated Learning is a platform that promotes the connectivity of intelligent systems with increased network capacity, service quality, accessibility of the network, and user experience. Blockchain is a technology that is exposed and can contribute to stability in IIoT. Blockchain appears to be a mechanism to preserve IIoT and retain the confidentiality of user/data, and the capacity to provide unauthorized reproductive and information services. The emerging needs have centered on data storage, computation, and low latency management in potentially smart cities, transport, smart grids, and a wide number of sustainable environments.

The book Demystifying Federated Learning for Blockchain and Industrial Internet of Things has 10 chapters focused on various dimensions of Federated Learning for Blockchain and IIoT from different perspectives, i.e., introductory topics, uses of big data in Blockchain and Federated Learning, Edge computing and many more. The main objective of this book is to rediscover, redefine, and reestablish the most recent applications of Federated Learning using blockchain and IIoT to optimize data for next-generation networks. The book contains 10 chapters in total which covers variety of topics, i.e., Federated based traffic offloading prediction and optimization, Blockchain and IoT applications.

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