Isbn: 9781032738970 - federated learning: unlocking the power of collaborative intelligence (9 risultati)

Lingua: Inglese
Editore: CRC Press, 2026
Serie: Libro 37 di 48 - Chapman & Hall/CRC Artificial Intelligence and Robotics
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Lingua: Inglese
Editore: CRC Press, 2026
Serie: Libro 37 di 48 - Chapman & Hall/CRC Artificial Intelligence and Robotics
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Lingua: Inglese
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Lingua: Inglese
Editore: Taylor & Francis Ltd, 2026
Serie: Libro 37 di 48 - Chapman & Hall/CRC Artificial Intelligence and Robotics
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Paperback. Condizione: Brand New. 182 pages. 6.14x0.41x9.21 inches. In Stock.

Lingua: Inglese
Editore: CRC Press, 2026
Serie: Libro 37 di 48 - Chapman & Hall/CRC Artificial Intelligence and Robotics
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Condizione: New. M. Irfan Uddin is currently working as a faculty member at the Institute of Computing, Kohat University of Science and Technology, Kohat, Pakistan. He has received his academic qualifications in computer science and has worked as a researcher on f.

Lingua: Inglese
Editore: Taylor & Francis Ltd (Sales) Jul 2026, 2026
Serie: Libro 37 di 48 - Chapman & Hall/CRC Artificial Intelligence and Robotics
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Taschenbuch. Condizione: Neu. Neuware - Federated Learning: Unlocking the Power of Collaborative Intelligence is a definitive guide to the transformative potential of federated learning. This book delves into federated learning principles, techniques, and applications, and offers practical insights and real-world case studies to showcase its capabilities and benefits.The book begins with a survey of the fundamentals of federated learning and its significance in the era of privacy concerns and data decentralization. Through clear explanations and illustrative examples, the book presents various federated learning frameworks, architectures, and communication protocols. Privacy-preserving mechanisms are also explored, such as differential privacy and secure aggregation, offering the practical knowledge needed to address privacy challenges in federated learning systems. This book concludes by highlighting the challenges and emerging trends in federated learning, emphasizing the importance of trust, fairness, and accountability, and provides insights into scalability and efficiency considerations.With detailed case studies and step-by-step implementation guides, this book shows how to build and deploy federated learning systems in real-world scenarios - such as in healthcare, finance, Internet of things (IoT), and edge computing. Whether you are a researcher, a data scientist, or a professional exploring the potential of federated learning, this book will empower you with the knowledge and practical tools needed to unlock the power of federated learning and harness the collaborative intelligence of distributed systems.Key Features: - Provides a comprehensive guide on tools and techniques of federated learning - Highlights many practical real-world examples - Includes easy-to-understand explanations.…

Lingua: Inglese
Editore: Taylor & Francis Ltd, London, 2026
Serie: Libro 37 di 48 - Chapman & Hall/CRC Artificial Intelligence and Robotics
- Brossura
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Paperback. Condizione: new. Paperback. Federated Learning: Unlocking the Power of Collaborative Intelligence is a definitive guide to the transformative potential of federated learning. This book delves into federated learning principles, techniques, and applications, and offers practical insights and real-world case studies to showcase its capabilities and benefits.The book begins with a survey of the fundamentals of federated learning and its significance in the era of privacy concerns and data decentralization. Through clear explanations and illustrative examples, the book presents various federated learning frameworks, architectures, and communication protocols. Privacy-preserving mechanisms are also explored, such as differential privacy and secure aggregation, offering the practical knowledge needed to address privacy challenges in federated learning systems. This book concludes by highlighting the challenges and emerging trends in federated learning, emphasizing the importance of trust, fairness, and accountability, and provides insights into scalability and efficiency considerations.With detailed case studies and step-by-step implementation guides, this book shows how to build and deploy federated learning systems in real-world scenarios such as in healthcare, finance, Internet of things (IoT), and edge computing. Whether you are a researcher, a data scientist, or a professional exploring the potential of federated learning, this book will empower you with the knowledge and practical tools needed to unlock the power of federated learning and harness the collaborative intelligence of distributed systems.Key Features:Provides a comprehensive guide on tools and techniques of federated learningHighlights many practical real-world examplesIncludes easy-to-understand explanations With detailed case studies and step-by-step implementation guides, this book shows how to build and deploy federated learning systems in real-world scenarios such as in healthcare, finance, IoT, and edge computing. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Lingua: Inglese
Editore: Taylor & Francis Ltd, London, 2026
Serie: Libro 37 di 48 - Chapman & Hall/CRC Artificial Intelligence and Robotics
- Brossura
- Print on Demand
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Paperback. Condizione: new. Paperback. Federated Learning: Unlocking the Power of Collaborative Intelligence is a definitive guide to the transformative potential of federated learning. This book delves into federated learning principles, techniques, and applications, and offers practical insights and real-world case studies to showcase its capabilities and benefits.The book begins with a survey of the fundamentals of federated learning and its significance in the era of privacy concerns and data decentralization. Through clear explanations and illustrative examples, the book presents various federated learning frameworks, architectures, and communication protocols. Privacy-preserving mechanisms are also explored, such as differential privacy and secure aggregation, offering the practical knowledge needed to address privacy challenges in federated learning systems. This book concludes by highlighting the challenges and emerging trends in federated learning, emphasizing the importance of trust, fairness, and accountability, and provides insights into scalability and efficiency considerations.With detailed case studies and step-by-step implementation guides, this book shows how to build and deploy federated learning systems in real-world scenarios such as in healthcare, finance, Internet of things (IoT), and edge computing. Whether you are a researcher, a data scientist, or a professional exploring the potential of federated learning, this book will empower you with the knowledge and practical tools needed to unlock the power of federated learning and harness the collaborative intelligence of distributed systems.Key Features:Provides a comprehensive guide on tools and techniques of federated learningHighlights many practical real-world examplesIncludes easy-to-understand explanations With detailed case studies and step-by-step implementation guides, this book shows how to build and deploy federated learning systems in real-world scenarios such as in healthcare, finance, IoT, and edge computing. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Lingua: Inglese
Editore: Taylor & Francis Ltd, London, 2026
Serie: Libro 37 di 48 - Chapman & Hall/CRC Artificial Intelligence and Robotics
- Brossura
- Print on Demand
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Paperback. Condizione: new. Paperback. Federated Learning: Unlocking the Power of Collaborative Intelligence is a definitive guide to the transformative potential of federated learning. This book delves into federated learning principles, techniques, and applications, and offers practical insights and real-world case studies to showcase its capabilities and benefits.The book begins with a survey of the fundamentals of federated learning and its significance in the era of privacy concerns and data decentralization. Through clear explanations and illustrative examples, the book presents various federated learning frameworks, architectures, and communication protocols. Privacy-preserving mechanisms are also explored, such as differential privacy and secure aggregation, offering the practical knowledge needed to address privacy challenges in federated learning systems. This book concludes by highlighting the challenges and emerging trends in federated learning, emphasizing the importance of trust, fairness, and accountability, and provides insights into scalability and efficiency considerations.With detailed case studies and step-by-step implementation guides, this book shows how to build and deploy federated learning systems in real-world scenarios such as in healthcare, finance, Internet of things (IoT), and edge computing. Whether you are a researcher, a data scientist, or a professional exploring the potential of federated learning, this book will empower you with the knowledge and practical tools needed to unlock the power of federated learning and harness the collaborative intelligence of distributed systems.Key Features:Provides a comprehensive guide on tools and techniques of federated learningHighlights many practical real-world examplesIncludes easy-to-understand explanations With detailed case studies and step-by-step implementation guides, this book shows how to build and deploy federated learning systems in real-world scenarios such as in healthcare, finance, IoT, and edge computing. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…