Artificial Intelligence Using Federated Learning: Fundamentals, Challenges, and Applications (Intelligent Manufacturing and Industrial Engineering)

By: Ahmed A. Elngar and Diego Oliva and Valentina E. Balas

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Synopsis: 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.Artificial Intelligence Using Federated Learning: Fundamentals, Challenges, and Applications enables training AI models on a large number of decentralized devices or servers, making it a scalable and efficient solution. It also allows organizations to create more versatile AI models by training them on data from diverse sources or domains. This approach can unlock innovative use cases in fields like healthcare, finance, and IoT, where data privacy is paramount.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.

Book Quality:: Publisher Quality
ISBN-13:: 9781040266717
Related ISBNs:: 9781040266694, 9781003482000, 9781032771649
Publisher:: CRC Press
Date of Addition:: 12/30/24
Copyrighted By:: selection and editorial matter, Ahmed A. Elngar, Diego Oliva and Valentina E. Balas
Adult content:: No
Language:: English
Has Image Descriptions:: No
Categories:: Nonfiction, Computers and Internet, Technology
Submitted By:: Bookshare Staff
Usage Restrictions:: This is a copyrighted book.
Edited by:: Ahmed A Elngar
Edited by:: Diego Oliva
Edited by:: Valentina E. Balas