株式会社極東書店トップ ＞ 商品一覧 ＞ Federated Deep Learning for Healthcare: A Practical Guide with Challenges and Opportunities.
商品詳細
Federated Deep Learning for Healthcare: A Practical Guide with Challenges and Opportunities.
・ISBN 978-1-032-68955-5 hard GB£ 145.99
￥46,249.- (税込) ※（※）価格はご注文時の参考価格となります。
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| 著者・編者 | Kaur, Amandeep / Kaushal, Chetna / Hassan, Md. Mehedi / Aung, Si Thu (eds.), |
|---|---|
| シリーズ | (Advances in Smart Healthcare Technologies) |
| 出版社 | (CRC Press, UK) |
| 出版年月 | 2024 |
| ページ数 | 252 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A02-94423＞ |
解説
This book provides a practical guide to federated deep learning for healthcare including fundamental concepts, framework, and the applications comprising domain adaptation, model distillation, and transfer learning. It covers concerns in model fairness, data bias, regulatory compliance, and ethical dilemmas. It investigates several privacy-preserving methods such as homomorphic encryption, secure multi-party computation, and differential privacy. It will enable readers to build and implement federated learning systems that safeguard private medical information.
Features:
- Offers a thorough introduction of federated deep learning methods designed exclusively for medical applications.
- Investigates privacy-preserving methods with emphasis on data security and privacy.
- Discusses healthcare scaling and resource efficiency considerations.
- Examines methods for sharing information among various healthcare organizations while retaining model performance.
This book is aimed at graduate students and researchers in federated learning, data science, AI/machine learning, and healthcare.