Federated Learning - Couverture rigide
Livre 35 sur 37: Chapman & Hall/CRC Artificial Intelligence and Robotics
Synopsis
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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
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 funded projects. He is involved in teaching and research activities related to different diverse computer science topics and has more than 18 years of teaching plus research experience. He is a member of IEEE, ACM, and HiPEAC. He has organized national and international seminars, workshops, and conferences. He has published over a hundred research papers in international journals and conferences. His research interests include machine learning, data science, artificial neural networks, deep learning, convolutional neural networks, recurrent neural networks, attention models, reinforcement learning, generative adversarial networks, computer vision, image processing, machine translation, natural language processing, speech recognition, big data analytics, parallel programming, multi-core, many-core, and GPUs.
Wali Khan Mashwani received an M.Sc. degree in mathematics from the University of Peshawar, Khyber Pakhtunkhwa, Pakistan, in 1996, and a Ph.D. degree in mathematics from the University of Essex, UK, in 2012. He is currently a Professor of Mathematics and the Director of the Institute of Numerical Sciences, Kohat University of Science and Technology (KUST), Khyber Pakhtunkhwa. He is also a Dean of the Physical and Numerical Science faculty at KUST. He has published more than 100 academic papers in peer-reviewed international journals and conference proceedings. His research interests include evolutionary computation, hybrid evolutionary multi-objective algorithms, decomposition-based evolutionary methods for multi-objective optimization, mathematical programming, numerical analysis, and artificial neural networks.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Federated Learning (Chapman & Hall/CRC Artificial Intelligence and Robotics Series)
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CRC Press, 2024
ISBN 10 : 1032724323
ISBN 13 : 9781032724324
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Federated Learning : Unlocking the Power of Collaborative Intelligence
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CRC Press, 2024
ISBN 10 : 1032724323
ISBN 13 : 9781032724324
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Federated Learning (Hardcover)
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Hardcover. Etat : new. Hardcover. 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. N° de réf. du vendeur 9781032724324
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Federated Learning: Unlocking the Power of Collaborative Intelligence (Chapman & Hall/CRC Artificial Intelligence and Robotics Series)
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Federated Learning
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CRC Press Sep 2024, 2024
ISBN 10 : 1032724323
ISBN 13 : 9781032724324
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - 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 asdifferential 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 194 pp. Englisch. N° de réf. du vendeur 9781032724324
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Federated Learning : Unlocking the Power of Collaborative Intelligence
Edité par
CRC Press, 2024
ISBN 10 : 1032724323
ISBN 13 : 9781032724324
Ancien ou d'occasion
Couverture rigide
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