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Advances and Open Problems in Federated Learning (Paperback)
Loot Price: R2,235
Discovery Miles 22 350
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Advances and Open Problems in Federated Learning (Paperback)
Series: Foundations and Trends (R) in Machine Learning
Expected to ship within 10 - 15 working days
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The term Federated Learning was coined as recently as 2016 to
describe a machine learning setting where multiple entities
collaborate in solving a machine learning problem, under the
coordination of a central server or service provider. Each client's
raw data is stored locally and not exchanged or transferred;
instead, focused updates intended for immediate aggregation are
used to achieve the learning objective. Since then, the topic has
gathered much interest across many different disciplines and the
realization that solving many of these interdisciplinary problems
likely requires not just machine learning but techniques from
distributed optimization, cryptography, security, differential
privacy, fairness, compressed sensing, systems, information theory,
statistics, and more. This monograph has contributions from leading
experts across the disciplines, who describe the latest
state-of-the art from their perspective. These contributions have
been carefully curated into a comprehensive treatment that enables
the reader to understand the work that has been done and get
pointers to where effort is required to solve many of the problems
before Federated Learning can become a reality in practical
systems. Researchers working in the area of distributed systems
will find this monograph an enlightening read that may inspire them
to work on the many challenging issues that are outlined. This
monograph will get the reader up to speed quickly and easily on
what is likely to become an increasingly important topic: Federated
Learning.
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