000 03532cam a2200505 i 4500
001 9781003482000
003 FlBoTFG
005 20251030122252.0
006 m o d
007 cr cnu---unuuu
008 241119s2025 flua ob 001 0 eng
040 _aOCoLC-P
_beng
_erda
_cOCoLC-P
020 _a9781003482000
_qelectronic book
020 _a1003482007
_qelectronic book
020 _a9781040266694
_qelectronic book
020 _a104026669X
_qelectronic book
020 _z9781032771649
_qhardcover
020 _z9781032772462
_qpaperback
035 _a(OCoLC)1472989443
035 _a(OCoLC-P)1472989443
050 0 4 _aTA347.A78
_bA879 2025
245 0 0 _aArtificial intelligence using federated learning :
_bfundamentals, challenges, and applications /
_cedited by Ahmed A. Elngar, Diego Oliva and Valentina E. Balas.
250 _aFirst edition.
264 1 _aBoca Raton, FL :
_bCRC Press,
_c2025.
300 _a1 online resource (xiv, 294 pages) :
_billustrations (chiefly color).
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 0 _aIntelligent manufacturing and industrial engineering
505 2 _aBlockchain-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 .
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"--
_cProvided by publisher.
588 _aOCLC-licensed vendor bibliographic record.
650 0 _aArtificial intelligence
_xIndustrial applications.
_965268
650 0 _aArtificial intelligence
_xSecurity measures.
_995048
650 0 _aFederated learning (Machine learning)
_994903
650 0 _aPrivacy-preserving techniques (Computer science)
_995049
700 1 _aElngar, Ahmed A.,
_eeditor.
_1https://id.oclc.org/worldcat/entity/E39PCjwHFXCGdYr9yxWCHFDtTd
_995050
700 1 _aOliva, Diego,
_eeditor.
_1https://id.oclc.org/worldcat/entity/E39PCjGMQ9tcvGpw34yRgm87gX
_995051
700 1 _aBalas, Valentina Emilia,
_eeditor.
_1https://id.oclc.org/worldcat/entity/E39PBJjCPfdqQJyMpDymx8gdwC
_964218
856 4 0 _3Taylor & Francis
_uhttps://www.taylorfrancis.com/books/9781003482000
856 4 2 _3OCLC metadata license agreement
_uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
942 _cE-Books
999 _c67957
_d67957