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 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.

Coverage:
The many academic areas covered in this publication include, but are not limited to:
•Deep Reinforcement Learning
•Edge IoT Networks
•Educational Big Data
•Fog-Internet of Things
•Human-Computer Interaction
•Multi-Criteria Decision Making
•Object Identification
•Remotely Assisted Robotic Surgery
•Security Issues
•Smart Agriculture