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Federated Learning - A Comprehensive Overview of Methods and Applications (Hardcover, 1st ed. 2022)
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Federated Learning - A Comprehensive Overview of Methods and Applications (Hardcover, 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.
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