A Novel Approach for Differential Privacy-Preserving Federated Learning
In this paper, we start with a comprehensive evaluation of the effect of adding differential privacy (DP) to federated learning (FL) approaches, focusing on methodologies employing global (stochastic) gradient descent (SGD/GD), and local SGD/GD techniques.These global and local dea eyewear techniques are commonly referred to as FedSGD/FedGD and Fed