Approach | Technique | Main Idea |
Geyer et al. [28] | FL + CDP | Initiate the study of federated learning with central differential privacy and verify the validity of the model by numerical experiments |
Triastcyn and Faltings [29] | Bayesian differential privacy | Develop a relaxation of federated learning with central differential privacy, named Bayesian differential privacy. |
Wei et al. [30] | NbAFL | Develop a novel framework (named NbAFL) based on DP in which artificial noises are added |
Zhang et al. [32] | Clipping-enabled FedAvg | Develop a novel central differential privacy framework (named clipping-enabled FedAvg) based on clipping technique. |
Hu et al. [33] | Fed-SMP | Develop a novel framework, named Fed-SMP, to mitigate the inaccuracy issue of LDP by using a technique called Sparsified Model Perturbation (SMP) where local models are sparsified first before being perturbed by Gaussian noise. |