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020 a| 9781040266717q| ebook;

020 a| 9781003482000q| (ebk);

020 z| 9781032771649q| (hbk);

020 z| 9781032772462q| (pbk);

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245 00 a| Artificial intelligence using federated learningh| [electronic resource] :b| fundamentals, challenges, and applications /c| edited by Ahmed A. Elngar, Diego Oliva and Valentina E. Balas.;

250 a| First edition.;

260 a| Boca Raton, FL :b| CRC Press, Taylor & Francis Group,c| 2025.;

300 a| 1 online resource.;

490 0 a| Intelligent manufacturing and industrial engineering;

504 a| Includes bibliographical references and index.;

505 2 a| Blockchain-enhanced federated learning for privacy-preserving collaboration / Pawan Whig, Balaram Yadav Kasula, Nikhitha Yathiraju, Anupriya Jain, Seema Sharma and Ahmed A. Elngar -- In-depth analysis of artificial intelligence practices : robot tutors and federated learning approach in english education / Muthmainnah, Akkarapon Nuemaihom, Ahmad Al Yakin, Prodhan Mahbub Ibna Seraj, Muhammad Mujtaba Asad and Ahmed A. Elngar -- Enabling federated learning in the classroom : sociotechnical ecosystem on artificial intelligence integration in educational practices / Ahmad Al Yakin, Arkas Viddy, Idi Warsah, Ali Said Al Matari, Luís Cardoso, Ahmed A. Elngar, Ahmad J. Obaid and Muthmainnah .;

506 a| Available only to authorized users.;

520 a| "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"--c| Provided by publisher.;

538 a| Mode of access: World Wide Web;

588 a| Description based on print version record and CIP data provided by publisher.;

650 0 a| Artificial intelligencex| Industrial applications.=| ^A15394;

650 0 a| Artificial intelligencex| Security measures.=| ^A15394;

650 0 a| Federated learning (Machine learning)?| UNAUTHORIZED;

650 0 a| Privacy-preserving techniques (Computer science)?| UNAUTHORIZED;

655 0 a| Electronic books.=| ^A491897;

700 1 a| Elngar, Ahmed A.=| ^A1458018;

700 1 a| Oliva, Diego.?| UNAUTHORIZED;

700 1 a| Balas, Valentina Emilia.=| ^A1333321;

776 08 i| Print version:t| Artificial intelligence using federated learningb| First edition.d| Boca Raton, FL : CRC Press, Taylor & Francis Group, 2025z| 9781032771649w| (DLC) 2024033619;

856 40 z| Full text available from eBooks on EBSCOhostu| https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3985904&scope=site&custid=s5822723&authtype=ip,shib;

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999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6741950-3001l| MNETm| JMUSICr| Ys| Yt| MNESSBKu| 7/15/2026x| EBOOKz| JERESOURCE;

Artificial intelligence using federated learning fundamentals, challenges, and applications / edited by Ahmed A. Elngar, Diego Oliva and Valentina E. Balas.

Other author	Elngar, Ahmed A.
Other author	Oliva, Diego.
Other author	Balas, Valentina Emilia.
Format	Electronic
Edition	First edition.
Publication Info	Boca Raton, FL : CRC Press, Taylor & Francis Group, 2025.
Description	1 online resource.
Supplemental Content	Full text available from eBooks on EBSCOhost
Subjects	Artificial intelligence--Industrial applications. Artificial intelligence--Security measures. Federated learning (Machine learning) Privacy-preserving techniques (Computer science)

Series	Intelligent manufacturing and industrial engineering
Partial contents	Blockchain-enhanced federated learning for privacy-preserving collaboration / Pawan Whig, Balaram Yadav Kasula, Nikhitha Yathiraju, Anupriya Jain, Seema Sharma and Ahmed A. Elngar -- In-depth analysis of artificial intelligence practices : robot tutors and federated learning approach in english education / Muthmainnah, Akkarapon Nuemaihom, Ahmad Al Yakin, Prodhan Mahbub Ibna Seraj, Muhammad Mujtaba Asad and Ahmed A. Elngar -- Enabling federated learning in the classroom : sociotechnical ecosystem on artificial intelligence integration in educational practices / Ahmad Al Yakin, Arkas Viddy, Idi Warsah, Ali Said Al Matari, Luís Cardoso, Ahmed A. Elngar, Ahmad J. Obaid and Muthmainnah .
Abstract	"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"-- Provided by publisher.
Bibliography note	Includes bibliographical references and index.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Source of description	Description based on print version record and CIP data provided by publisher.
Issued in other form	Print version: Artificial intelligence using federated learning First edition. Boca Raton, FL : CRC Press, Taylor & Francis Group, 2025 9781032771649
Genre/form	Electronic books.
LCCN	2024033620
ISBN	9781040266717 ebook
ISBN	9781003482000 (ebk)
ISBN	(hbk)
ISBN	(pbk)

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Artificial intelligence using federated learning fundamentals, challenges, and applications / edited by Ahmed A. Elngar, Diego Oliva and Valentina E. Balas.

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