Dettaglio del documento
Federated learning : a primer for mathematicians
Abstract
This book serves as a primer on a secure computing framework known as federated learning. Federated learning is the study of methods to enable multiple parties to collaboratively train machine learning/AI models, while each party retains its own, raw data on-premise, never sharing it with others [...].
[...]
It covers the basics of topics from computer science that are needed to understand examples of simple federated computing frameworks. It is my hope that by learning basic concepts and technical jargon from computer science, readers will be able to start collaborative work with researchers interested in secure computing [...]. (Estratto dal sito dell'autore)
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| eBook | ||
| Monografia | ||
| Description | *Federated learning : a primer for mathematicians / Mei Kobayashi. - Singapore : Springer, 2025. - 1 testo elettronico (xiv, 82 p. : ill.) | |
| ISBN E-Book | 9789819692231 | |
| Series | ICIAM2023 Springer series , 4 | |
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| Subjects | 68-XX - Computer science [MSC 2020] | |
| 68P25 - Data encryption [MSC 2020] | ||
| 68P27 - Privacy of data [MSC 2020] | ||
| 68T07 - Artificial neural networks and deep learning [MSC 2020] | ||
| Parole chiave |
Artificial Intelligence
Cloudlets Data Augmentation Data Security Data leakage Data poisoning Edge Computing Federated Learning Federated transfer learning Fog Computing Multiparty computation Neural Networks Quantum safe encryption Synthetic data Transfer Learning |
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| Publication place | Singapore | |
| Publisher | Springer <editore> | |
| Publication year | 2025 | |
| Thesaurus | 68-XX | |
| 68P25 | ||
| 68P27 | ||
| 68T07 | ||
| Artificial neural networks and deep learning [MSC 2020] | ||
| Computer science [MSC 2020] | ||
| Data encryption [MSC 2020] | ||
| Privacy of data [MSC 2020] |