9781032738970 - federated learning: unlocking the power of collaborative intelligence (chapman & hall/crc artificial intelligence and robotics series) (4 resultados)
Idioma: Inglés
Editorial: CRC Press 2026
Serie: Chapman & Hall/CRC Artificial Intelligence and Robotics, Libro 37 de 48. Libro 37 de 48 - Chapman & Hall/CRC Artificial Intelligence and Robotics
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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EUR 107,61
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Condición: New.
Idioma: Inglés
Editorial: CRC Press 2026
Serie: Chapman & Hall/CRC Artificial Intelligence and Robotics, Libro 37 de 48. Libro 37 de 48 - Chapman & Hall/CRC Artificial Intelligence and Robotics
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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EUR 106,43
Envío por EUR 9,95Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New.
Idioma: Inglés
Editorial: Taylor & Francis Ltd, London 2026
Serie: Chapman & Hall/CRC Artificial Intelligence and Robotics, Libro 37 de 48. Libro 37 de 48 - Chapman & Hall/CRC Artificial Intelligence and Robotics
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Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
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EUR 87,87
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Paperback. Condición: 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 s…howcase 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.
Idioma: Inglés
Editorial: Taylor & Francis Ltd, London 2026
Serie: Chapman & Hall/CRC Artificial Intelligence and Robotics, Libro 37 de 48. Libro 37 de 48 - Chapman & Hall/CRC Artificial Intelligence and Robotics
- Tapa blanda
- Impresión bajo demanda
Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail
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EUR 83,67
Envío por EUR 42,95Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: 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 s…howcase 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.
