株式会社極東書店トップ ＞ 商品一覧 ＞ Artificial Intelligence Using Federated Learning: Fundamentals, Challenges, and Applications.
商品詳細
Artificial Intelligence Using Federated Learning: Fundamentals, Challenges, and Applications.
・ISBN 978-1-032-77246-2 paper GB£ 55.99
￥17,737.- (税込) ※（※）価格はご注文時の参考価格となります。
納品価格につきましては書籍の入荷時点で確定となります。
版元の原価改定、外国為替の変動等により異なる場合がございますので、予めご了承下さい。
| 著者・編者 | Elngar, Ahmed A / Oliva, Diego / Balas, Valentina E. (eds.), |
|---|---|
| シリーズ | (Intelligent Manufacturing and Industrial Engineering) |
| 出版社 | (CRC Press, UK) |
| 出版年月 | 2026 |
| ページ数 | 294 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A05-77330＞ |
解説
Federated machine learning is a novel approach to combining distributed machine learning, cryptography, security, and incentive mechanism design. It allows organizations to keep sensitive and private data on users or customers decentralized and secure, helping them comply with stringent data protection regulations like GDPR and CCPA.
Artificial Intelligence Using Federated Learning: Fundamentals, Challenges, and Applications enables training AI models on a large number of decentralized devices or servers, making it a scalable and efficient solution. It also allows organizations to create more versatile AI models by training them on data from diverse sources or domains. This approach can unlock innovative use cases in fields like healthcare, finance, and IoT, where data privacy is paramount.
The book is designed for researchers working in Intelligent Federated Learning and its related applications, as well as technology development, and is also of interest to academicians, data scientists, industrial professionals, researchers, and students.