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Federated and Transfer Learning - Adaptation, Learning, and Optimization 1st ed. 2023 edition
Federated and Transfer Learning - Adaptation, Learning, and Optimization
This book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of federated learning as well as its connection with transfer learning and reinforcement learning.
371 pages, 80 Illustrations, color; 10 Illustrations, black and white; VIII, 371 p. 90 illus., 80 il
| メディア | 書籍 Hardcover Book (ハードカバー付きの本) |
| リリース済み | 2022年10月1日 |
| ISBN13 | 9783031117473 |
| 出版社 | Springer International Publishing AG |
| ページ数 | 371 |
| 寸法 | 150 × 220 × 20 mm · 735 g |
| 言語 | ドイツ語 |
| 編集者 | Razavi-Far, Roozbeh |
| 編集者 | Taylor, Matthew E. |
| 編集者 | Wang, Boyu |
| 編集者 | Yang, Qiang |