Most differentially private federated learning methods are still fundamentally first-order: clip per-example gradients, add Gaussian noise, aggregate, and take a step. DP-FedGD and DP-FedAvg follow this directly. DP-FedAdam, DP-FedYogi, and DP-SCAFFOLD improve the dynamics around the step, but they'
