1 INTRODUCTION

2 FEDERATED LEARNING: AN OVERVIEW  
2.1 FL Categorization  
2.2 FL Phases  

3 PRIVACY CHALLENGES IN FL  
3.1 Inferring Class Representatives  
3.2 Inferring Membership  
3.3 Inferring Data Properties  
3.4 Inferring Samples / Labels  

4 PRIVACY-PRESERVATION IN FL  
4.1 Cryptographic Methods  
4.2 Anonymization Methods  
4.3 Perturbative Methods  
4.4 Hardware-Based Protection  
4.5 Hybrid Privacy-Preserving Techniques  

5 PRIVACY PRESERVING METRICS  

6 FAIRNESS IN FEDERATED LEARNING  
6.1 Causes of Bias in FL  

7 RECENT WORKS IN FL-FAIRNESS  
7.1 Fairness Challenges in Horizontal and Vertical Federated Settings  
7.2 Notions of Fairness in Federated Learning  
7.3 Client Selection  
7.4 Group Fairness  
7.5 Processing Algorithms for Eradicating Group Fairness  
7.6 Notions of Group Fairness  

8 FAIRNESS EVALUATION METRICS  

9 PRESERVING MODEL FAIRNESS, ACCURACY AND PRIVACY  

10 CHALLENGES AND FUTURE DIRECTIONS IN FAIRNESS AND PRIVACY PRESERVATION IN FL  

11 CONCLUSION