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

Contributor(s): Material type: TextSeries: Intelligent manufacturing and industrial engineeringPublisher: Boca Raton, FL : CRC Press, 2025Edition: First editionDescription: 1 online resource (xiv, 294 pages) : illustrations (chiefly color)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781003482000
  • 1003482007
  • 9781040266694
  • 104026669X
Subject(s): LOC classification:
  • TA347.A78 A879 2025
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 .
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"-- Provided by publisher.
Item type: List(s) this item appears in: Titluri cărți electronice Taylor&Francis 2025 (1039 titluri)
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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 .

"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.

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