Federated Learning

Idioma: inglés

Editorial: Springer, Springer Jul 2022, 2022

3030968952 / 9783030968953

Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.

Vendedor de 5 estrellas

Vendedor de IberLibro desde 11 de enero de 2012

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Descripción del artículo del vendedor

This item is printed on demand - it takes 3-4 days longer - Neuware -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-art methods.Following an overview on federated learning basics in the introduction, over the following 24 chapters, the reader will dive deeply into various topics. A first part addresses algorithmic questions of solving different machine learning tasks in a federated way, how to train efficiently, at scale, and fairly. Another part focuses on providing clarity on how to select privacy and security solutions in a way that can be tailored to specific use cases, while yet another considers the pragmatics of the systems where the federated learning process will run. The book also covers other important use cases for federated learning such as split learning and vertical federated learning. Finally, the book includes some chapters focusing on applying FL in real-world enterprise settings. 544 pp. Englisch.…

N° de ref. del artículo 9783030968953

Título
Federated Learning
Autor
Heiko Ludwig
Editorial
Springer, Springer Jul 2022
Año de publicación
2022
Estado
Neu
Encuadernación
Buch
Idioma
inglés
ISBN 10
3030968952
ISBN 13
9783030968953
Peso del artículo
975 gramos
Dimensiones
241x160x35 mm

BuchWeltWeit Ludwig Meier e.K.

Bergisch Gladbach, Alemania

Vendedor de 5 estrellas

Vendedor de IberLibro desde 11 de enero de 2012

Tarifas de envío de Alemania a Estados Unidos de America

ArtículoDe 5 a 15 días hábilesDe 5 a 15 días hábiles
Primer artículoEUR 23,00EUR 23,00
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Información empresarial del vendedor

BuchWeltWeit Ludwig Meier e.K.

Alemania