Federated Learning: Theory and Practice

Federated Learning: Theory and Practi ce is a book that provides a comprehensive treatment of federated learning, a distributed learning system with decentralized data and features. It covers optimization fundamentals, modeling challenges, emerging concerns, industrial applications, and ethical considerations, showcasing its potential for driving innovation and safeguarding sensitive data.

Format: Paperback / softback
Length: 434 pages
Publication date: 01 January 2024
Publisher: Elsevier Science Publishing Co Inc

Federated Learning: Theory and Practi ce is a comprehensive guide to federated learning, a distributed learning system that harnesses the power of decentralized data and features. This book offers a holistic treatment of federated learning, covering various aspects such as optimization fundamentals, modeling challenges, communication efficiency, theoretical convergence, and security.

In Part I, the book provides a broad overview of optimization fundamentals and modeling challenges. It explores various aspects of communication efficiency, theoretical convergence, and security, which are crucial for the successful implementation of federated learning. The authors delve into the intricacies of these topics, providing insights and methodologies that can help practitioners overcome challenges and achieve optimal performance.

Part II of the book focuses on emerging challenges stemming from many socially driven concerns of federated learning as a future public machine learning service. The authors address topics such as privacy, fairness, and accountability, which are critical for the development and deployment of federated learning systems. They discuss the potential risks and ethical considerations associated with federated learning and propose solutions to mitigate these challenges.

Part III concludes the book with a wide array of industrial applicati ons of federated learning. The authors showcase real-world examples and case studies that demonstrate the immense potential of federated learning in various domains, such as healthcare, finance, and social media. They discuss the practical considerations and challenges involved in implementing federated learning systems and provide insights into how practitioners can leverage this technology to drive innovation while safeguarding sensitive data.

Federated Learning: Theory and Practi ce is a valuable resource for researchers and students in academia, as well as industrial practitioners who seek to leverage the latest advance in machine learning for their entrepreneurial endeavors. The book offers a comprehensive and accessible introducti on to federated learning, covering all the essential topics and providing practical insights and methodologies that can help practitioners build effective and robust federated learning systems.

In conclusion, Federated Learning: Theory and Practi ce is a must-read for anyone interested in the field of machine learning. It provides a comprehensive and accessible treatment of federated learning, covering optimization fundamentals, modeling challenges, communication efficiency, theoretical convergence, and security. The book also addresses emerging challenges and industrial applicati ons, showcasing the immense potential of federated learning for driving innovation while safeguarding sensitive data. Whether you are a researcher, student, or practitioner, this book will help you gain a deeper understanding of federated learning and its applications in various domains.

Weight: 450g
Dimension: 234 x 191 (mm)
ISBN-13: 9780443190377