fundamentals, challenges, and applications Elngar, Ahmed A. editor. Oliva, Diego editor. Balas, Valentina Emilia editor. text bibliography flu 2025 First edition. monographic eng 1 online resource (xiv, 294 pages) : illustrations (chiefly color). "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"-- 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 . edited by Ahmed A. Elngar, Diego Oliva and Valentina E. Balas. Artificial intelligence Industrial applications Artificial intelligence Security measures Federated learning (Machine learning) Privacy-preserving techniques (Computer science) TA347.A78 A879 2025 9781003482000 1003482007 9781040266694 104026669X https://www.taylorfrancis.com/books/9781003482000 http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf https://www.taylorfrancis.com/books/9781003482000 http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf OCoLC-P 241119 20251030122252.0 9781003482000 eng