1 INTRODUCTION  
2 BACKGROUND  
2.1 Federated Learning  
2.2 Knowledge Distillation  
2.3 KD-based FL  
3 KNOWLEDGE-DISTILLATION-BASED FEDERATED LEARNING  
3.1 KD-based FL: Motivation  
3.2 How KD tackles the challenges of FL  
3.3 KD-based FL: Taxonomy  
3.4 KD-based FL: Comparison with traditional FL  
3.5 KD-based FL: Where should KD execute  
4 HOW KD-BASED FL TACKLES THE LONG-LASTING CHALLENGES  
4.2 KD-based FL for non-IID  
4.3 KD-based FL for communication efficiency  
4.4 KD-based FL for personalization  
5 FUTURE DIRECTION  
6 CONCLUSION