## 6 Discussion  
In this study, the HFL framework showed great improvements over conventional FL in the aspects of scalability, computation, and privacy in various healthcare scenarios. The HFL framework proposed in this work achieves an approximately 97% accuracy and reduces training time and privacy leakage, thus solving several key issues in FL systems, especially in healthcare big data applications.  
The results indicate that the HFL framework is more efficient than the existing models, including FLBM-IoT and PPFLB, as depicted in Figure 3. In particular, for HFL we  
Figure 6: Comparison of utility–privacy trade-off.  
observed a 15% improvement in training time and a 30% improvement in privacy leakage, which implies that the multi-level aggregation process not only improves scalability but also decreases the cost of communication. This is consistent with the findings made that constraint is a common drawback of FL frameworks, such as in study [28], who highlighted the impact of communication efficiency on federated learning performance. By introducing regional aggregation, our model mitigates the communication bottleneck that often hampers traditional FL frameworks.  
Furthermore, the use of improved privacy-preserving techniques like LDP, GDP, SMPC, and HE enables the provision of high-level privacy. The combination of these techniques helps in reducing the probability of privacy leakage, as observed in our experiments, with lower privacy risks. In contrast to prior work, the HFL framework offers a reasonable trade-off between privacy and model performance, as pointed out in [29] who emphasized the necessity for adaptive privacy mechanisms in FL, and [30], who stressed the importance of dynamic privacy mechanisms in FL.  
The HFL framework proposed in this paper has important consequences for the secure and efficient development of machine learning in healthcare settings. The privacy features designed into the HFL architecture enable healthcare organizations to use sensitive patient data in different institutions and still protect the privacy of patients. This capability is particularly important as the issue of data privacy has become more important in healthcare, especially due to HIPAA in the United States and GDPR in Europe. Further, the proposed HFL framework achieves scalability by cutting down the communication load through a two-level aggregation process, thus enabling the deployment of the framework across large, geographically dispersed healthcare systems. The results indicate that HFL can be a viable solution for healthcare organizations that are interested in working together on massive data analytics while preserving their data confidentiality.  
However, there are some limitations of the present study that need to be highlighted. First, the experiments were performed on a small number of real and synthetic healthcare datasets and did not cover all the possible variations in healthcare data across different geographic locations and organizations. Future work should involve applying the HFL framework to other healthcare datasets originating from different medical specializations and healthcare organizations in order to validate the framework. The proposed Aggregated Gradient Perturbation Mechanism not only achieves a good trade-off between privacy and utility but also results in a computational cost that may be infeasible in some environments. Other possibilities include model compression and efficient encryption techniques that can minimize the computational load of the HFL framework, as pointed out by [32].