Federated Learning / Lecture Notes in Computer Science Bd.12500 (PDF)
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This book contains three main parts. First, it introduces different privacy-preserving methods for protecting a Federated Learning model against different types of attacks such as Data Leakage and/or Data Poisoning. Second, the book presents incentive mechanisms which aim to encourage individuals to participate in the Federated Learning ecosystems. Last but not the least, this book also describeshow Federated Learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both academia and industries, who would like to learn federated learning from scratch, practice its implementation, and apply it in their own business.
Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing are preferred.
- 2020, 1st ed. 2020, 286 Seiten, Englisch
- Herausgegeben: Qiang Yang, Lixin Fan, Han Yu
- Verlag: Springer International Publishing
- ISBN-10: 3030630765
- ISBN-13: 9783030630768
- Erscheinungsdatum: 25.11.2020
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
- Dateiformat: PDF
- Größe: 26 MB
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