Federated Learning

Name: Federated Learning
Brand: Springer Nature Switzerland AG
SKU: 9783030968953
Price: 57642 HUF
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A Comprehensive Overview of Methods and Applications

Szerző: Heiko Ludwig, Nathalie Baracaldo

Nyelv:

Kötés: Kemény kötésű

Kiadó: Springer Nature Switzerland AG

Elérhetőség: Beszállítói készleten

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Federated Learning: A Comprehensive Overview of Methods and Applications presents an in-depth discus...

Információk a könyvről

Szerző

Heiko Ludwig, Nathalie Baracaldo

Nyelv

Angol

Kötés

Könyv - Kemény kötésű

Kiadva

2022

oldal

534

EAN

9783030968953

Enbook ID

38623080

Kiadó

Springer Nature Switzerland AG

Súly

975

Méretek

155 x 235 x 35

Teljes leírás

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.

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Federated Learning

A Comprehensive Overview of Methods and Applications

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