The Gaussian mechanism used for differential privacy in H2FDP may have limitations and drawbacks that could impact its effectiveness in certain scenarios. One potential limitation is the sensitivity of the mechanism to outliers, which can lead to inaccurate privacy guarantees and compromised data protection. Additionally, the Gaussian mechanism may introduce significant noise that hinders the utility of the model, affecting the overall performance of the federated learning system. To address these limitations, alternative DP mechanisms such as Laplace noise or Exponential mechanism could be incorporated into the H2FDP framework. These mechanisms offer different noise distributions that may provide better privacy guarantees while minimizing the impact on model accuracy. By exploring a variety of DP mechanisms and selecting the most suitable one based on the specific requirements of the federated learning task, H2FDP can enhance the privacy-utility tradeoff and improve the overall performance of the system.