Security and privacy in federated learning / Shui Yu, Lei Cui.
Yu, Shui (Computer scientist), author.; Cui, Lei, author.
2023
QA76.9.A25
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Title Security and privacy in federated learning / Shui Yu, Lei Cui.
Author Yu, Shui (Computer scientist), author.
ISBN 9789811986925 (electronic bk.)
9811986924 (electronic bk.)
9789811986918
9811986916
DOI https://doi.org/10.1007/978-981-19-8692-5
Published Singapore : Springer, 2023.
Language English
Description 1 online resource (170 pages) : illustrations (some color).
Item Number 10.1007/978-981-19-8692-5 doi
Call Number QA76.9.A25
Dewey Decimal Classification 005.8
Summary In this book, the authors highlight the latest research findings on the security and privacy of federated learning systems. The main attacks and counterattacks in this booming field are presented to readers in connection with inference, poisoning, generative adversarial networks, differential privacy, secure multi-party computation, homomorphic encryption, and shuffle, respectively. The book offers an essential overview for researchers who are new to the field, while also equipping them to explore this uncharted territory. For each topic, the authors first present the key concepts, followed by the most important issues and solutions, with appropriate references for further reading. The book is self-contained, and all chapters can be read independently. It offers a valuable resource for masters students, upper undergraduates, Ph.D. students, and practicing engineers alike.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Added Author Cui, Lei, author.
Series Digital privacy and security.
Available in Other Form Security and privacy in federated learning.
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Record Appears in Online Resources > Ebooks
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Chapter 1. Introduction of Federated Learning
Chapter 2. Inference Attacks and Counter Attacks in Federated Learning
Chapter 3. Poisoning Attacks and Counter Attacks in Federated Learning
Chapter 4. GAN Attacks and Counter Attacks in Federated Learning
Chapter 5. Differential Privacy in Federated Learning
Chapter 6. Secure Multi-Party Computation in Federated Learning
Chapter 7. Secure Data Aggregation in Federated Learning
Chapter 8. Anonymous Communication and Shuffle Model in Federated Learning
Chapter 9. The Future Work.

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