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Federated Learning: Privacy and Incentive - Yang, Qiang (Editor), and Fan, Lixin (Editor), and Yu, Han (Editor)
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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 ...

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Federated Learning: Privacy and Incentive 2020, Springer, Cham

ISBN-13: 9783030630751

2020 edition

Trade paperback