| 書(shū)目名稱(chēng) | Federated and Transfer Learning |
| 編輯 | Roozbeh Razavi-Far,Boyu Wang,Qiang Yang |
| 視頻video | http://file.papertrans.cn/342/341598/341598.mp4 |
| 概述 | Helps readers to understand transfer learning in conjunction with federated learning.Bridges the gap between transfer learning and federated learning.Performs a comprehensive study on the recent advan |
| 叢書(shū)名稱(chēng) | Adaptation, Learning, and Optimization |
| 圖書(shū)封面 |  |
| 描述 | .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. Over the last few years, the machine learning community has become fascinated by federated and transfer learning. Transfer and federated learning have achieved great success and popularity in many different fields of application. The intended audience of this book is students and academics aiming to apply federated and transfer learning to solve different kinds of real-world problems, as well as scientists, researchers, and practitioners in AI industries, autonomous vehicles, and cyber-physical systems who wish to pursue new scientific innovations and update their knowledge on federated and transfer learning and their applications.. |
| 出版日期 | Book 2023 |
| 關(guān)鍵詞 | Transfer Learning; Federated Learning; Domain Adaptation; Zero-shot Learning; One-shot Learning; Multitas |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-031-11748-0 |
| isbn_softcover | 978-3-031-11750-3 |
| isbn_ebook | 978-3-031-11748-0Series ISSN 1867-4534 Series E-ISSN 1867-4542 |
| issn_series | 1867-4534 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
|Archiver|手機(jī)版|小黑屋|
派博傳思國(guó)際
( 京公網(wǎng)安備110108008328)
GMT+8, 2025-10-14 18:54
發(fā)表于 2025-3-21 16:34:05
