Order Total (1 Item Items):
Shipping Destination:
Tripathy Somanath (53 results)
Skip to main search results
Search filters
Product Type
- All Product Types
- Books (53)
- Magazines & Periodicals (No further results match this refinement)
- Comics (No further results match this refinement)
- Sheet Music (No further results match this refinement)
- Art, Prints & Posters (No further results match this refinement)
- Photographs (No further results match this refinement)
- Maps (No further results match this refinement)
- Manuscripts & Paper Collectibles (No further results match this refinement)
Condition Learn more
- New (46)
- As New, Fine or Near Fine (6)
- Very Good or Good (1)
- Fair or Poor (No further results match this refinement)
- As Described (No further results match this refinement)
Binding
Collectible Attributes
- First Edition (1)
- Signed (No further results match this refinement)
- Dust Jacket (No further results match this refinement)
- Seller-Supplied Images (19)
- Not Print on Demand (44)
Language (1)
Free Shipping
Seller Location
Seller Rating
-
-
-
-
Condition: As New. Unread book in perfect condition.
-
-
Condition: New.
-
Federated Learning
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 4 out of 5 stars
HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
-
HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
-
Condition: As New. Unread book in perfect condition.
-
Applications of Machine Learning in Big-data Analytics and Cloud Computing
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
-
Applications of Machine Learning in Big-data Analytics and Cloud Computing
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
-
Federated Learning
Language: English
Published by Taylor and Francis Ltd, GB, 2025
ISBN 10: 1041174624 ISBN 13: 9781041174622
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
Hardback. Condition: New. As data becomes more abundant and widespread across personal devices, the need for secure, privacy-aware machine learning is growing. Federated Learning (FL) offers a promising solution, enabling smart devices to collaboratively train models without sharing raw data. Yet, despite its benefits, FL faces serious risks from poisoning and inference attacks.This book begins by introducing the fundamentals of machine learning, along with core deep learning architectures. Based on this foundation, it introduces the concept of Federated Learning (FL), which is a decentralised approach that enables collaborative model training without sharing raw data. The book provides an in-depth exploration of FL's various forms, system architectures, and practical applications. A significant emphasis is placed on the growing security and privacy concerns in FL, particularly poisoning (both data poisoning and model poisoning) and inference attacks. It discusses state-of-the-art mitigation strategies, such as Byzantine-robust aggregation and inference-resistant techniques, supported with practical implementation insights.This book uniquely bridges foundational concepts with advanced topics in Federated Learning, offering a comprehensive view of its vulnerabilities and their mitigation. By combining theory with practical implementation of attacks and mitigation techniques, it serves as a valuable resource for researchers, practitioners, and students aiming to build secure, privacy-preserving collaborative machine learning systems.This book is unique due to its end-to-end coverage of Federated Learning (FL), from foundational machine and deep learning concepts to real-time deployment of FL along with security and privacy challenges associated. It both explains theory and offers hands-on implementation of attacks and defenses. This practical approach, combined with a clear structure and real-world relevance, makes it ideal for both academic and industry audiences. Promotional emphasis should highlight the book's focus on actionable insights, its relevance to privacy-preserving and secure AI, and its utility as a learning and reference tool for building secure collaborative learning systems.
-
Federated Learning : Security and Privacy
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
-
Condition: New.
-
Federated Learning
Language: English
Published by Taylor and Francis Ltd, GB, 2025
ISBN 10: 1041174624 ISBN 13: 9781041174622
Hardback. Condition: New. As data becomes more abundant and widespread across personal devices, the need for secure, privacy-aware machine learning is growing. Federated Learning (FL) offers a promising solution, enabling smart devices to collaboratively train models without sharing raw data. Yet, despite its benefits, FL faces serious risks from poisoning and inference attacks.This book begins by introducing the fundamentals of machine learning, along with core deep learning architectures. Based on this foundation, it introduces the concept of Federated Learning (FL), which is a decentralised approach that enables collaborative model training without sharing raw data. The book provides an in-depth exploration of FL's various forms, system architectures, and practical applications. A significant emphasis is placed on the growing security and privacy concerns in FL, particularly poisoning (both data poisoning and model poisoning) and inference attacks. It discusses state-of-the-art mitigation strategies, such as Byzantine-robust aggregation and inference-resistant techniques, supported with practical implementation insights.This book uniquely bridges foundational concepts with advanced topics in Federated Learning, offering a comprehensive view of its vulnerabilities and their mitigation. By combining theory with practical implementation of attacks and mitigation techniques, it serves as a valuable resource for researchers, practitioners, and students aiming to build secure, privacy-preserving collaborative machine learning systems.This book is unique due to its end-to-end coverage of Federated Learning (FL), from foundational machine and deep learning concepts to real-time deployment of FL along with security and privacy challenges associated. It both explains theory and offers hands-on implementation of attacks and defenses. This practical approach, combined with a clear structure and real-world relevance, makes it ideal for both academic and industry audiences. Promotional emphasis should highlight the book's focus on actionable insights, its relevance to privacy-preserving and secure AI, and its utility as a learning and reference tool for building secure collaborative learning systems.
-
hardcover. Condition: New.
-
Condition: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.
-
Federated Learning: Security and Privacy
Language: English
Published by Taylor & Francis Ltd, 2025
ISBN 10: 1041174624 ISBN 13: 9781041174622
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
Hardback. Condition: New. New copy - Usually dispatched within 4 working days.
-
Federated Learning : Security and Privacy
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
-
-
Federated Learning: Security and Privacy
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
Seller rating 5 out of 5 stars
First Edition
Condition: New. 2025. 1st Edition. hardcover. . . . . .
-
-
Condition: New.
-
Applications of Machine Learning in Big-Data Analytics and Cloud Computing (River Publishers Series in Information Science and Technology)
Seller: Books From California, Simi Valley, CA, U.S.A.
Seller rating 4 out of 5 stars
Hardcover. Condition: Very Good. Clean, unmarked copy.
-
Condition: New.
-
Hardcover. Condition: Brand New. 120 pages. 8.50x5.43x8.74 inches. In Stock.
-
Condition: New. 2025. 1st Edition. hardcover. . . . . . Books ship from the US and Ireland.
-
Condition: New. Somanath Tripathy received his PhD from IIT Guwahati in 2007. Currently, he is a professor in the Department of Computer Science and Engineering at the Indian Institute of Technology, Patna, where he has been a faculty member since December 2008. .
-
Condition: As New. Unread book in perfect condition.
-
Federated Learning
Language: English
Published by Taylor and Francis Ltd, GB, 2025
ISBN 10: 1041174624 ISBN 13: 9781041174622
Hardback. Condition: New. As data becomes more abundant and widespread across personal devices, the need for secure, privacy-aware machine learning is growing. Federated Learning (FL) offers a promising solution, enabling smart devices to collaboratively train models without sharing raw data. Yet, despite its benefits, FL faces serious risks from poisoning and inference attacks.This book begins by introducing the fundamentals of machine learning, along with core deep learning architectures. Based on this foundation, it introduces the concept of Federated Learning (FL), which is a decentralised approach that enables collaborative model training without sharing raw data. The book provides an in-depth exploration of FL's various forms, system architectures, and practical applications. A significant emphasis is placed on the growing security and privacy concerns in FL, particularly poisoning (both data poisoning and model poisoning) and inference attacks. It discusses state-of-the-art mitigation strategies, such as Byzantine-robust aggregation and inference-resistant techniques, supported with practical implementation insights.This book uniquely bridges foundational concepts with advanced topics in Federated Learning, offering a comprehensive view of its vulnerabilities and their mitigation. By combining theory with practical implementation of attacks and mitigation techniques, it serves as a valuable resource for researchers, practitioners, and students aiming to build secure, privacy-preserving collaborative machine learning systems.This book is unique due to its end-to-end coverage of Federated Learning (FL), from foundational machine and deep learning concepts to real-time deployment of FL along with security and privacy challenges associated. It both explains theory and offers hands-on implementation of attacks and defenses. This practical approach, combined with a clear structure and real-world relevance, makes it ideal for both academic and industry audiences. Promotional emphasis should highlight the book's focus on actionable insights, its relevance to privacy-preserving and secure AI, and its utility as a learning and reference tool for building secure collaborative learning systems.
