One potential limitation of the gradient correction step in FedMeS is the sensitivity to noise in the local gradients, which can lead to unstable updates and hinder convergence. To address this limitation, regularization techniques can be applied to stabilize the gradient correction process and prevent overfitting to the local memory samples. Additionally, adaptive learning rate strategies can be implemented to dynamically adjust the learning rate during gradient correction based on the magnitude of the gradients and the historical performance of the model. This can help improve the robustness of the gradient correction step and enhance the overall training stability of FedMeS.