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Federated Learning - A Comprehensive Overview of Methods and Applications (1st ed. 2022)
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Federated Learning - A Comprehensive Overview of Methods and Applications (1st ed. 2022)
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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.
General
Imprint: |
Springer Nature Switzerland AG
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Country of origin: |
Switzerland |
Release date: |
July 2023 |
First published: |
2022 |
Editors: |
Heiko Ludwig
• Nathalie Baracaldo
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Dimensions: |
235 x 155mm (L x W) |
Pages: |
534 |
Edition: |
1st ed. 2022 |
ISBN-13: |
978-3-03-096898-4 |
Categories: |
Books
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LSN: |
3-03-096898-7 |
Barcode: |
9783030968984 |
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