Federated Learning for Multimedia Data Processing and Security in Industry 5.0
Language: English
Published by Institution of Engineering and Technology, GB, 2025
- Hardcover
- New
Seller: Rarewaves.com USA, London, London, United KingdomRarewaves.com USA
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Add to basketItem description from seller
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.…
Seller Inventory # LU-9781839537578
- Title
- Federated Learning for Multimedia Data Processing and Security in Industry 5.0
- Author
- Gautam Srivastava
- Publisher
- Institution of Engineering and Technology, GB
- Publication year
- 2025
- Condition
- New
- Binding
- Hardback
- Language
- English
- ISBN 10
- 1839537574
- ISBN 13
- 9781839537578
- Dimensions
- 15.6 x 2.06 x 23.39 cm
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.
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
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