Federated Learning for Multimedia Data Processing and Security in Industry 5.0

Gautam Srivastava is a full professor in the Department of Mathematics and Computer Science at Brandon University, Manitoba, Canada. He previously worked in the Department of Computer Science at the Faculty of Engineering, University of Victoria. His research focuses on data mining and big data security and privacy. He is involved in research projects with other academics in Norway, Taiwan, Singapore, Canada, and the USA. He has published 400+ papers in high-impact conferences and journals.Thippa Reddy Gadekallu is a chief engineer at Zhongda Group, Haiyan County, Jiaxing City, Zhejiang Province, China. He is also an associate professor in the School of Information Technology and Engineering at Vellore Institute of Technology, India. He has published 200+ publications in reputed journals and conferences. His areas of research include machine learning, deep neural networks, internet of things, blockchain and computer vision. He is an editor for several international journals.

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