Federated Learning Systems Towards Privacy-Preserving Distributed AI
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- Hardcover
- Taschenbuch
- eBook ausgewählt
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Form:Einzelkauf Download
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Sprache:Englisch
Fr. 213.90
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Nein
Erscheinungsdatum
26.04.2025
Herausgeber
Muhammad Habib Ur Rehman + weitereVerlag
Springer Nature SwitzerlandSeitenzahl
165 (Printausgabe)
Dateigröße
7475 KB
Sprache
Englisch
EAN
9783031788413
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.
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