Federated learning is a popular distributed machine learning framework in which clients aggregate their learned models without sharing their individual data under privacy-preserving consideration. However, FL is still facing many challenges, among which efficiency, accuracy, security and fairness challenges are the main problems that hinder the development of FL.
- Survey
- Datasets
- Efficiency Computation,Communication,Quantization
- Effectiveness Non_IID, Accuracy challenge, Convergence, Robustness
- Incentive
- Vertical FL
- Boosting
- Application