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Federated Learning: Theory and Practice

Federated Learning: Theory and Practice

Federated Learning: Theory and Practice Paperback - 2024

by Lam M. Nguyen (Editor); Trong Nghia Hoang (Editor); Pin-Yu Chen (Editor)

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Paperback. New. New Book; Fast Shipping from UK; Not signed; Not First Edition; 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 opt
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Details

  • Title Federated Learning: Theory and Practice
  • Author Lam M. Nguyen (Editor); Trong Nghia Hoang (Editor); Pin-Yu Chen (Editor)
  • Binding Paperback
  • Condition New
  • Pages 434
  • Volumes 1
  • Language ENG
  • Publisher Academic Press
  • Publication date 2024-02-15
  • Bookseller's Inventory # ria9780443190377_inp
  • ISBN 9780443190377 / 0443190372
  • Weight 1.64 lbs (0.74 kg)
  • Dimensions 9.25 x 7.5 x 0.89 in (23.50 x 19.05 x 2.26 cm)
  • Category Computers - General Information
  • Quantity available 426

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Reader reviews for Federated Learning: Theory and Practice

From the publisher

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 features
emerging 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.

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