Agricultural Supply Chain Using Federated Learning
Language: English
Published by Wiley Jan 2027, 2027
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 224.24
Quantity: 2 available
Add to basketItem description from seller
Neuware - Master the next evolution of agricultural intelligence with this definitive guide to federated learning, providing decentralized, privacy-preserving strategies needed to optimize global supply chains without compromising data sovereignty. As global agriculture faces challenges such as climate variability, resource inefficiency, and data privacy concerns, traditional centralized AI systems struggle to operate at scale. Federated learning addresses these limitations by enabling decentralized, privacy-preserving model training across distributed datasets, supporting secure and collaborative optimization. This book explores how federated learning enhances precision farming, logistics optimization, and sustainable resource management through real-time, data-driven decision-making while respecting local variations and regulatory constraints. It bridges the gap between advanced AI technologies and practical agricultural supply chain management, covering foundational concepts, system architectures, and real-world implementations. Through case studies and applied insights, the book demonstrates how federated learning can improve productivity, reduce waste, and strengthen sustainability while maintaining data sovereignty. It offers a balanced perspective on both technical and managerial aspects, making it accessible to a wide audience while retaining depth for academic and industry professionals.…
Seller Inventory # 9781394461264
- Title
- Agricultural Supply Chain Using Federated Learning
- Author
- S. Panneerselvam
- Publisher
- Wiley Jan 2027
- Publication year
- 2027
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 1394461267
- ISBN 13
- 9781394461264
- Item weight
- 694 grams
- Dimensions
- 229x152x26 mm
Master the next evolution of agricultural intelligence with this definitive guide to federated learning, providing decentralized, privacy-preserving strategies needed to optimize global supply chains without compromising data sovereignty.
As global agriculture faces challenges such as climate variability, resource inefficiency, and data privacy concerns, traditional centralized AI systems struggle to operate at scale. Federated learning addresses these limitations by enabling decentralized, privacy-preserving model training across distributed datasets, supporting secure and collaborative optimization. This book explores how federated learning enhances precision farming, logistics optimization, and sustainable resource management through real-time, data-driven decision-making while respecting local variations and regulatory constraints. It bridges the gap between advanced AI technologies and practical agricultural supply chain management, covering foundational concepts, system architectures, and real-world implementations. Through case studies and applied insights, the book demonstrates how federated learning can improve productivity, reduce waste, and strengthen sustainability while maintaining data sovereignty. It offers a balanced perspective on both technical and managerial aspects, making it accessible to a wide audience while retaining depth for academic and industry professionals.
"Synopsis" may belong to another edition of this title.
About the Author
Abhishek Kumar, PhD, is an Assistant Director and Professor in the Computer Science and Engineering Department at Chandigarh University with more than 13 years of teaching experience. He has authored seven books, edited 101 books, and published more than 220 peer-reviewed articles. His research spans AI, renewable energy, image processing, and data mining.
Pooja Dixit is an Assistant Professor in the Department of Computer Science at Shri Ratanlal Kanwarlal Patni Girls' College, Kishangarh, India. With more than ten years of academic teaching and two years of research experience, she has published more than 30 research papers in reputed journals, books, and conferences. Her research interests include artificial intelligence, machine learning, and data mining.
J.P. Ananth, PhD, is a Professor and Director of the Internal Quality Assurance Cell at Dayananda Sagar University with more than 23 years of experience. He serves as a reviewer for several international conferences and journals. His research interests include computer vision, pattern recognition, artificial intelligence, and data analytics.
S. Oswalt Manoj, PhD, is a Professor in the Department of Computer Science and Engineering at Alliance University with more than 14 years of experience. He has published more than 100 publications in reputed, peer-reviewed national and international journals and conferences, authored one book, and edited two books. His research areas include big data analytics, artificial intelligence, computer vision, machine learning, deep learning, and cloud computing.
S. Panneerselvam, PhD, is a Professor in the Department of Agricultural Engineering at the Hindustan College of Engineering and Technology. He has published 65 research articles, more than 12 books, and 20 book chapters.
"About the title" may belong to another edition of this title.
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