This book presents a comprehensive exploration of federated learning and its transformative potential across industries, focusing on privacy-preserving, decentralized AI solutions. It introduces novel frameworks and applications in healthcare, smart transportation, energy optimization, and Industry 4.0, emphasizing real-world use cases and addressing key challenges in privacy, scalability, and collaboration. By bridging theory and practice, the book provides actionable insights into implementing federated learning for dynamic, interconnected ecosystems like the Industrial Internet of Everything (IoE). Aimed at researchers, practitioners, and policymakers, it offers cutting-edge strategies to enhance efficiency, security, and innovation in diverse industrial domains.

Covers latest developments in Federated Learning, specifically in the context of the Industrial IoE Explores interdisciplinary approach, drawing from the fields of data science, engineering, and computer science Examination of the future trends and potential impact of Federated Learning in the Industrial IoE

Inhalt

.- Introduction to Federated Learning and its applications in the IoE.- Techniques used in Federated Learning.- Federated Learning in the Manufacturing Industry.- Federated Learning in the Transportation Industry.- Federated Learning in the Healthcare Industry.- Challenges and Opportunities in Federated Learning for the IoE.- Conclusion and future research directions, etc.