Übersicht			Auf mobile öffnen

Lieferstatus:	Auf Bestellung (Lieferzeit unbekannt)
Veröffentlichung:	Dezember 2024
Genre:	EDV / Informatik
	Artificial Intelligence / Automatic control engineering / Computer architecture & logic design / Computer architecture and logic design / COMPUTERS / Artificial Intelligence / General / COMPUTERS / Computer Architecture / COMPUTERS / Logic Design / COMPUTERS / Security / General / distributed learning systems / edge device collaboration / GDPR Compliance / Privacy & data protection / Privacy and data protection / privacy preserving AI / privacy preserving machine learning applications / Production and industrial engineering / Production engineering / semantic web integration / smart transportation analytics / Systems analysis & design / Systems analysis and design / TECHNOLOGY & ENGINEERING / Automation / TECHNOLOGY & ENGINEERING / Industrial Engineering / TECHNOLOGY & ENGINEERING / Manufacturing mehr...
ISBN:	9781032771649
EAN-Code:	9781032771649
Verlag:	Taylor and Francis
Einband:	Gebunden
Sprache:	English
Dimensionen:	H 234 mm / B 156 mm / D
Gewicht:	580 gr
Seiten:	294
Illustration:	schwarz-weiss Illustrationen, Raster,schwarz-weiss, Zeichnungen, schwarz-weiss, Tabellen, schwarz-weiss
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

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.