I. Introduction  
A. Related Surveys  
B. Contributions  
C. Organization  

II. Background  
A. Machine Unlearning  
B. Federated Learning  
C. Catastrophic Forgetting and Differential Privacy  

III. Federated Unlearning  
A. Objectives  
B. Why Is Unlearning Needed if Data Are Not Shared?  
C. Challenges in Federated Unlearning  
D. Entity Performing Unlearning  

IV. Metrics to Assess Unlearning  

V. Literature Review of Federated Unlearning Methods  
A. Client Unlearning  
B. Class Unlearning  
C. Sample Unlearning  

VI. Distilled Lessons Learned  

VII. Open Problems and Future Directions  
A. Standardization  
B. Incentive to Avoid Unlearning  
C. Multiple Unlearning Request  
D. Feature Unlearning  
E. Security and Privacy  
F. Data Heterogeneity Among Clients  

VIII. Conclusions