Übersicht			Auf mobile öffnen

Lieferstatus:	Auf Bestellung (Lieferzeit unbekannt)
Veröffentlichung:	April 2025
Genre:	Naturwissensch., Medizin, Technik
	Algorithms / Applications / Artificial Intelligence / Client Selection / Computational Intelligence / Federated Learning / Healthcare / Intrusion Detection Systems / Privacy / recommender systems / Security / Systems mehr...
ISBN:	9783031788406
EAN-Code:	9783031788406
Verlag:	Springer International Publishing
Einband:	Gebunden
Sprache:	English
Dimensionen:	H 235 mm / B 155 mm / D
Seiten:	165
Illustration:	XVIII, 165 p. 30 illus., 25 illus. in color., schwarz-weiss Illustrationen, farbige Illustrationen
Zus. Info:	EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025.
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

This book dives deep into both industry implementations and cutting-edge research driving the Federated Learning (FL) landscape forward. FL enables decentralized model training, preserves data privacy, and enhances security without relying on centralized datasets. Industry pioneers like NVIDIA have spearheaded the development of general-purpose FL platforms, revolutionizing how companies harness distributed data. Alternately, for medical AI, FL platforms, such as FedBioMed, enable collaborative model development across healthcare institutions to unlock massive value. Research advances in PETs highlight ongoing efforts to ensure that FL is robust, secure, and scalable. Looking ahead, federated learning could transform public health by enabling global collaboration on disease prevention while safeguarding individual privacy. From recommendation systems to cybersecurity applications, FL is poised to reshape multiple domains, driving a future where collaboration and privacy coexist seamlessly.