| 书目名称 | Federated Learning | | 副标题 | A Comprehensive Over | | 编辑 | Heiko Ludwig,Nathalie Baracaldo | | 视频video | | | 概述 | First major book on Federated Learning, and the standard text on the topic by the leading researchers worldwide.Federated Learning as a concept is only a few years old but has seen a rapid increase in | | 图书封面 |  | | 描述 | .Federated Learning: A Comprehensive Overview of Methods and Applications. presents an in-depth discussion of the most important issues and approaches to federated learning for researchers and practitioners. .Federated Learning (FL) is an approach to machine learning in which the training data are not managed centrally. Data are retained by data parties that participate in the FL process and are not shared with any other entity. This makes FL an increasingly popular solution for machine learning tasks for which bringing data together in a centralized repository is problematic, either for privacy, regulatory or practical reasons..This book explains recent progress in research and the state-of-the-art development of Federated Learning (FL), from the initial conception of the field to first applications and commercial use. To obtain this broad and deep overview, leading researchers address the different perspectives of federated learning: the core machine learning perspective, privacy and security, distributed systems, and specific application domains. Readers learn about the challenges faced in each of these areas, how they are interconnected, and how they are solved by state-of-the- | | 出版日期 | Book 2022 | | 关键词 | Deep Learning; Machine Learning; Artificial Intelligence; Vertically Partitioned Federated Learning; Neu | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-96896-0 | | isbn_softcover | 978-3-030-96898-4 | | isbn_ebook | 978-3-030-96896-0 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
The information of publication is updating
|
|
发表于 2025-3-21 16:49:41
