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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Federated Learning (Paperback)
Loot Price: R1,927
Discovery Miles 19 270
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Federated Learning (Paperback)
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning
Expected to ship within 10 - 15 working days
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How is it possible to allow multiple data owners to collaboratively
train and use a shared prediction model while keeping all the local
training data private? Traditional machine learning approaches need
to combine all data at one location, typically a data center, which
may very well violate the laws on user privacy and data
confidentiality. Today, many parts of the world demand that
technology companies treat user data carefully according to
user-privacy laws. The European Union's General Data Protection
Regulation (GDPR) is a prime example. In this book, we describe how
federated machine learning addresses this problem with novel
solutions combining distributed machine learning, cryptography and
security, and incentive mechanism design based on economic
principles and game theory. We explain different types of
privacy-preserving machine learning solutions and their
technological backgrounds, and highlight some representative
practical use cases. We show how federated learning can become the
foundation of next-generation machine learning that caters to
technological and societal needs for responsible AI development and
application.
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