Security and privacy in federated learning / Shui Yu, Lei Cui.
Yu, Shui (Computer scientist), author.; Cui, Lei, author.
2023
QA76.9.A25
Available Online

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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
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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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