Privacy estimation techniques for differentially private (DP) algorithms are useful for comparing against analytical bounds, or to empirically measure privacy loss insettings where known analytical bounds are not tight. However, existing privacy auditing techniques usually make strong assumptions on the adversary (e.g., knowl-edge of intermediate model iterates or the training data distribution), are tailored to specific tasks, model architectures, or DP algorithm, and/or require retraining the model many times (typically on the order of thousands). These shortcomings make deploying such techniques at scale difficult in practice, especially in federatedsettings where model training can take days or weeks. In this work, we present a novel “one-shot” approach that can systematically address these challenges, al-lowing efficient auditing or estimation of the privacy loss of a model during the same, single training run used to fit model parameters, and without requiring anyaprioriknowledge about the model architecture, task, or DP algorithm. We show that our method provides provably correct estimates for the privacy loss under the Gaussian mechanism, and we demonstrate its performance on a well-established FL benchmark dataset under several adversarial threat models.