1 Introduction
2 Background
2.1 Federated Learning
2.2 Transfer Learning
2.3 Federated Transfer Learning
3 Threats to Federated Learning
3.1 Threat Models
3.2 Attacks on Performance
3.3 Attacks on Privacy
4 Threats to Transfer Learning
4.1 Backdoor Attacks
4.2 Adversarial Attacks
4.3 Inference Attacks
5 Defense Mechanisms
5.1 Privacy Preserving
5.2 Model Robustness
6 Future Research
6.1 Decentralized Federated Transfer Learning
6.2 Flaws in Current Defense Mechanisms
6.3 Optimizing Defense Mechanism Deployment
6.4 Achieving Simultaneous Objectives
6.5 Heterogeneity of Federated Transfer Learning
7 Conclusion