Federated learning multimedia data (15 résultats)
Langue : anglais
Edité par The Institution of Engineering and Technology, 2025
- Couverture rigide
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EUR 123,15
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Langue : anglais
Edité par The Institution of Engineering and Technology, 2025
- Couverture rigide
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Langue : anglais
Edité par The Institution of Engineering and Technology, 2025
- Couverture rigide
Vendeur : California Books, Miami, FL, Etats-UnisCalifornia Books
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Etat : New.
Langue : anglais
Edité par Institution of Engineering and Technology, GB, 2025
- Couverture rigide
Vendeur : Rarewaves USA, OSWEGO, IL, Etats-UnisRarewaves USA
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Hardback. Etat : New. 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 missi…on 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.
Langue : anglais
Edité par The Institution of Engineering and Technology, 2025
- Couverture rigide
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-UniGreatBookPricesUK
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HRD. Etat : New. New Book. Shipped from UK. Established seller since 2000.
Langue : anglais
Edité par The Institution of Engineering and Technology, 2025
- Couverture rigide
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-UniGreatBookPricesUK
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EUR 147,51
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Etat : New.
Langue : anglais
Edité par The Institution of Engineering and Technology, 2025
- Couverture rigide
Vendeur : Ria Christie Collections, Uxbridge, Royaume-UniRia Christie Collections
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Etat : New. In.
Langue : anglais
Edité par Institution of Engineering and Technology, GB, 2025
- Couverture rigide
Vendeur : Rarewaves.com USA, London, LONDO, Royaume-UniRarewaves.com USA
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EUR 176,16
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Hardback. Etat : New. 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 missi…on 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.
Federated Learning for Multimedia Data Processing and Security in Industry 5.0
Srivastava, Gautam (Editor)/ Gadekallu, Thippa Reddy (Editor)
- Couverture rigide
Vendeur : Revaluation Books, Exeter, Royaume-UniRevaluation Books
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EUR 169,85
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Hardcover. Etat : Brand New. 350 pages. 9.21x6.14x9.21 inches. In Stock.
Langue : anglais
Edité par Institution of Engineering and Technology, GB, 2025
- Couverture rigide
Vendeur : Rarewaves USA United, OSWEGO, IL, Etats-UnisRarewaves USA United
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EUR 148,03
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Hardback. Etat : New. 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 missi…on 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.
Langue : anglais
Edité par Institution Of Engineering & Technology Jan 2025, 2025
- Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, AllemagneAHA-BUCH GmbH
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EUR 175,00
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Buch. Etat : Neu. Neuware - 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.
Langue : anglais
Edité par Institution of Engineering and Technology, GB, 2025
- Couverture rigide
Vendeur : Rarewaves.com UK, London, Royaume-UniRarewaves.com UK
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
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Hardback. Etat : New. 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 missi…on 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.
- Couverture rigide
- impression à la demande
Vendeur : THE SAINT BOOKSTORE, Southport, Royaume-UniTHE SAINT BOOKSTORE
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Hardback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
