Verwandte Artikel zu Federated Learning Systems: Towards Privacy-Preserving...
Federated Learning Systems: Towards Privacy-Preserving Distributed AI: 832 (Studies in Computational Intelligence) - Softcover
Inhaltsangabe
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.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
Von der hinteren Coverseite
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.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
Suchergebnisse für Federated Learning Systems: Towards Privacy-Preserving...
Foto des Verkäufers
Federated Learning Systems | Towards Privacy-Preserving Distributed AI
Anbieter: preigu, Osnabrück, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. Federated Learning Systems | Towards Privacy-Preserving Distributed AI | Muhammad Habib Ur Rehman (u. a.) | Taschenbuch | xviii | Englisch | 2026 | Springer | EAN 9783031788437 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Artikel-Nr. 135781429
Neu kaufen
EUR 158,05
EUR 70,00 Versand
Versand von Deutschland nach USA
Versand von Deutschland nach USA
Anzahl: 5 verfügbar
Foto des Verkäufers
Federated Learning Systems : Towards Privacy-Preserving Distributed AI
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book dives deep into both industry implementations and cutting-edge research driving theFederated 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. Artikel-Nr. 9783031788437
Neu kaufen
EUR 206,69
EUR 35,00 Versand
Versand von Deutschland nach USA
Versand von Deutschland nach USA
Anzahl: 1 verfügbar