Artificial intelligence using federated learning : fundamentals, challenges, and applications /

"Federated machine learning is a novel approach to combining distributed machine learning, cryptography, security, and incentive mechanism design. It allows organizations to keep sensitive and private data on users or customers decentralized and secure, helping them comply with stringent data p...

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Bibliographic Details
Corporate Author:	Taylor & Francis
Other Authors:	Elngar, Ahmed A. (Editor), Oliva, Diego (Editor), Balas, Valentina Emilia (Editor)
Format:	eBook
Language:	English
Published:	Boca Raton, FL : CRC Press, 2025.
Edition:	First edition.
Series:	Intelligent manufacturing and industrial engineering
Subjects:	Artificial intelligence > Industrial applications. Artificial intelligence > Security measures. Federated learning (Machine learning) Privacy-preserving techniques (Computer science) Intelligence artificielle > Applications industrielles. Intelligence artificielle > Sécurité > Mesures. Electronic books.
Online Access:	Connect to the full text of this electronic book

Description
Summary:	"Federated machine learning is a novel approach to combining distributed machine learning, cryptography, security, and incentive mechanism design. It allows organizations to keep sensitive and private data on users or customers decentralized and secure, helping them comply with stringent data protection regulations like GDPR and CCPA. The book is designed for researchers working in Intelligent Federated Learning and its related applications, as well as technology development, and is also of interest to academicians, data scientists, industrial professionals, researchers, and students"--
Physical Description:	1 online resource (xiv, 294 pages) : illustrations (chiefly color).
Bibliography:	Includes bibliographical references and index.
ISBN:	9781003482000 1003482007 9781040266694 104026669X