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Federated Learning - Privacy and Incentive (Paperback, 1st ed. 2020)
Loot Price: R2,321
Discovery Miles 23 210
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Federated Learning - Privacy and Incentive (Paperback, 1st ed. 2020)
Series: Lecture Notes in Computer Science, 12500
Expected to ship within 10 - 15 working days
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This book provides a comprehensive and self-contained introduction
to federated learning, ranging from the basic knowledge and
theories to various key applications. Privacy and incentive issues
are the focus of this book. It is timely as federated learning is
becoming popular after the release of the General Data Protection
Regulation (GDPR). Since federated learning aims to enable a
machine model to be collaboratively trained without each party
exposing private data to others. This setting adheres to regulatory
requirements of data privacy protection such as GDPR. This book
contains three main parts. Firstly, it introduces different
privacy-preserving methods for protecting a federated learning
model against different types of attacks such as data leakage
and/or data poisoning. Secondly, the book presents incentive
mechanisms which aim to encourage individuals to participate in the
federated learning ecosystems. Last but not least, this book also
describes how federated learning can be applied in industry and
business to address data silo and privacy-preserving problems. The
book is intended for readers from both the academia and the
industry, who would like to learn about federated learning,
practice its implementation, and apply it in their own business.
Readers are expected to have some basic understanding of linear
algebra, calculus, and neural network. Additionally, domain
knowledge in FinTech and marketing would be helpful."
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