Federated Learning for Multimedia Data Processing and Security in Industry 5.0

Industry 5.0 is the upcoming industrial revolution where people will be working together with smart machines and robots, thereby bringing human touch and intelligence back to the decision-making process. Challenges include the security and privacy of sensitive multimedia data and near zero latency for mission critical applications. Federated learning is a machine learning technique that trains algorithms across multiple decentralized edge devices or servers by holding local data samples without exchanging them. This approach stands in contrast to traditional centralized machine learning techniques where all local datasets are uploaded to one server. This method enables multiple actors to build a common, robust machine learning model without sharing data, thus addressing critical issues such as data privacy, data security, data access rights and access to heterogeneous data. The objective of this book is to show how federated learning can solve multimedia data processing and security challenges in Industry 5.0. The book introduces new research paradigms for the security and privacy preservation of multimedia data. It provides a detailed discussion on how federated learning can be used to handle big data, preserve privacy, reduce computational and communication costs; and shows how to integrate federated learning with other disruptive technologies including blockchain, digital twins and 5G and beyond. Federated Learning for Multimedia Data Processing and Security in Industry 5.0 is an essential reference for advanced students, lecturers, and academic and industry researchers working in the fields of machine learning federated learning, computer and network security, data science, multimedia, computer vision and Industry 5.0 applications.

Table of Contents:
Chapter 1: Federated learning-enabled 5G and beyond for Industry 5.0 Chapter 2: Federated learning for optimized communication in Industry 5.0 Chapter 3: Federated learning enabled digital twins for Industry 5.0: perspectives, challenges, and future directions Chapter 4: Collaborative intelligence: federated learning-enabled edge computing in Industry 5.0 Chapter 5: Infusion of federated learning for cybersecurity in Industry 5.0 Chapter 6: Cybersecurity with blockchain for digital twins Chapter 7: Blockchain-based federated learning for Industry 5.0 applications Chapter 8: Blockchain implementation of public key infrastructure for Industry 5.0 applications Chapter 9: Federated learning for Supply Chain Management 5.0 Chapter 10: Federated learning for cobots in Industry 5.0 Chapter 11: Federated learning in medical education in Industry 5.0 Chapter 12: A comprehensive survey on enhanced privacy techniques for federated learning in healthcare systems Chapter 13: FL for secured medical image analysis Conclusion