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Barnes and Noble

Federated Learning: Privacy and Incentive

Current price: $84.99
Federated Learning: Privacy and Incentive
Federated Learning: Privacy and Incentive

Barnes and Noble

Federated Learning: Privacy and Incentive

Current price: $84.99
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Size: Paperback

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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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Barnes & Noble does business -- big business -- by the book. As the #1 bookseller in the US, it operates about 720 Barnes & Noble superstores (selling books, music, movies, and gifts) throughout all 50 US states and Washington, DC. The stores are typically 10,000 to 60,000 sq. ft. and stock between 60,000 and 200,000 book titles. Many of its locations contain Starbucks cafes, as well as music departments that carry more than 30,000 titles.

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