1 Introduction
2 Preliminaries of Federated Learning
3 Data to Model Attacks
3.1 D2M Attacks on Class Labels
3.2 D2M Attacks on Samples
3.3 Defense Against D2M Attacks
3.4 Evaluation Metrics for Attacks and Defenses on Classification Tasks
4 Model to Model Attacks
4.1 General M2M Threat Models
4.2 M2M Threat Models on Federated Recommendation Systems
4.3 Defense Against M2M Attack
5 Model to Data Attacks
5.1 Non-Gradient-Based Data Leakage
5.2 Gradient-Based Data Leakage
5.3 Defense Against M2D Attacks
6 Composite Attacks
6.1 Composite Threat Models
6.2 Defense Against Composite Attack
7 Conclusion and Future Directions
7.1 Conclusion
7.2 Future Directions