TY  - BOOK
AU  - Elngar,Ahmed A.
AU  - Oliva,Diego
AU  - Balas,Valentina Emilia
TI  - Artificial intelligence using federated learning: fundamentals, challenges, and applications 
T2  - Intelligent manufacturing and industrial engineering
SN  - 9781003482000
AV  - TA347.A78 A879 2025
PY  - 2025///
CY  - Boca Raton, FL
PB  - CRC Press
KW  - Artificial intelligence
KW  - Industrial applications
KW  - Security measures
KW  - Federated learning (Machine learning)
KW  - Privacy-preserving techniques (Computer science)
N1  - 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
N2  - "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"--
UR  - https://www.taylorfrancis.com/books/9781003482000
UR  - http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
ER  -