Federated Learning: Unlocking the Power of Collaborative Intelligence by M. Irfa

Description: Federated Learning by M. Irfan Uddin, Wali Khan Mashwani Estimated delivery 3-12 business days Format Hardcover Condition Brand New Description 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. Publisher Description 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 Author Biography 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. Details ISBN 1032724323 ISBN-13 9781032724324 Title Federated Learning Author M. Irfan Uddin, Wali Khan Mashwani Format Hardcover Year 2024 Pages 182 Publisher Taylor & Francis Ltd GE_Item_ID:161066200; About Us Grand Eagle Retail is the ideal place for all your shopping needs! With fast shipping, low prices, friendly service and over 1,000,000 in stock items - you're bound to find what you want, at a price you'll love! Shipping & Delivery Times Shipping is FREE to any address in USA. Please view eBay estimated delivery times at the top of the listing. Deliveries are made by either USPS or Courier. We are unable to deliver faster than stated. International deliveries will take 1-6 weeks. NOTE: We are unable to offer combined shipping for multiple items purchased. This is because our items are shipped from different locations. Returns If you wish to return an item, please consult our Returns Policy as below: Please contact Customer Services and request "Return Authorisation" before you send your item back to us. Unauthorised returns will not be accepted. Returns must be postmarked within 4 business days of authorisation and must be in resellable condition. Returns are shipped at the customer's risk. We cannot take responsibility for items which are lost or damaged in transit. For purchases where a shipping charge was paid, there will be no refund of the original shipping charge. Additional Questions If you have any questions please feel free to Contact Us. Categories Baby Books Electronics Fashion Games Health & Beauty Home, Garden & Pets Movies Music Sports & Outdoors Toys

Price: 163.44 USD

Location: Fairfield, Ohio

End Time: 2024-11-14T14:19:37.000Z

Shipping Cost: 0 USD