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Security and Privacy in Federated Learning

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 privac...

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Bibliographic Details
Main Authors: Yu, Shui, Cui, Lei (Author)
Format: eBook
Language:English
Published: Singapore Springer Nature Singapore 2023, 2023
Edition:1st ed. 2023
Series:Digital Privacy and Security
Subjects:
Artificial Intelligence
Privacy
Data Protection
Data Protection / Law And Legislation
Data And Information Security
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Table of Contents:
  • 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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