Federated Learning: Theory and Practice
by: Lam M. Nguyen (Editor),Trong Nghia Hoang (Editor),Pin-Yu Chen (Editor)&1more
Publisher:
Edition: 1st
Publication Date: 2024/2/29
Language: English
Print Length: 434 pages
ISBN-10: 0443190372
ISBN-13: 9780443190377
Book Description
Federated Learning: Theory and Practi ce provides a holisti c treatment to federated learning as a distributed learning system with various forms of decentralized data and features. Part I of the book begins with a broad overview of opti mizati on fundamentals and modeling challenges, covering various aspects of communicati on effi ciency, theoretical convergence, and security. Part II featuresemerging challenges stemming from many socially driven concerns of federated learning as a future public machine learning service. Part III concludes the book with a wide array of industrial applicati ons of federated learning, as well as ethical considerations, showcasing its immense potential for driving innovation while safeguarding sensitive data.Federated Learning: Theory and Practi ce provides a comprehensive and accessible introducti on to federated learning which is suitable for researchers and students in academia, and industrial practitioners who seek to leverage the latest advance in machine learning for their entrepreneurial endeavors.Presents the fundamentals and a survey of key developments in the field of federated learningProvides emerging, state-of-the art topics that build on fundamentalsContains industry applicationsGives an overview of visions of the future
About the Author
Federated Learning: Theory and Practi ce provides a holisti c treatment to federated learning as a distributed learning system with various forms of decentralized data and features. Part I of the book begins with a broad overview of opti mizati on fundamentals and modeling challenges, covering various aspects of communicati on effi ciency, theoretical convergence, and security. Part II featuresemerging challenges stemming from many socially driven concerns of federated learning as a future public machine learning service. Part III concludes the book with a wide array of industrial applicati ons of federated learning, as well as ethical considerations, showcasing its immense potential for driving innovation while safeguarding sensitive data.Federated Learning: Theory and Practi ce provides a comprehensive and accessible introducti on to federated learning which is suitable for researchers and students in academia, and industrial practitioners who seek to leverage the latest advance in machine learning for their entrepreneurial endeavors.Presents the fundamentals and a survey of key developments in the field of federated learningProvides emerging, state-of-the art topics that build on fundamentalsContains industry applicationsGives an overview of visions of the future Read more
未经允许不得转载：电子书百科大全 » Federated Learning: Theory and Practice