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.

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 master's students, upper undergraduates, Ph.D. students, and practicing engineers alike.