## 6 Discussion  
This experiment confirmed that transitioning the existing centralized post-TAVR complication prediction solution to a federated learning environment did not result in performance degradation. This illustrates that the advocated methodology permits AI solutions conventionally implemented in a centralized manner to be proficiently utilized in a federated learning context without compromising efficacy. The ability to train models without directly sharing data effectively addresses data privacy concerns that have been a major barrier to the advancement of AI technology. Overcoming this issue will contribute significantly to the broader adoption of AI technologies.  
Furthermore, the minimal performance disparity between centralized processing and federated learning implies the prospect of enduring performance enhancement, particularly considering the federated learning paradigm's capacity to augment data as additional institutions engage. This resolution, furnished with lifecycle management functionalities, facilitates sustainable model training, administration, and deployment, possessing the potential to function as a cornerstone for constructing a progressively evolving global model over time.  
The investigation employed non-IID data from three virtual institutions, and the findings indicated that the global model trained via federated learning was more generalizable and exhibited superior performance in numerous instances compared to the local models trained on each institution's biased data. This suggests that the imbalance and uneven distribution of non-IID data across institutions can be mitigated using a federated learning approach. Through the federated learning lifecycle, FedOps serves as a practical solution, enabling the full utilization of non-IID data from diverse institutions. This suggests that the platform is capable of effectively addressing data imbalance issues across multiple organizations in real-world federated learning applications.  
The methodology proposed in this study provides a universal and practical solution for transitioning conventional centralized artificial intelligence (AI) solutions in the medical field into federated learning (FL) systems that enable multi-institutional collaboration. This approach goes beyond theoretical contributions, offering actionable guidance for institutions or hospitals seeking to adopt federated learning while preserving the core functionalities and methodologies of their existing AI solutions. The experimental results demonstrate the feasibility of extending current solutions into systems that support multi-institutional participation, maintaining their foundational capabilities. These findings provide practical and detailed guidelines for converting centralized AI systems into collaborative multi-institutional solution, emphasizing their applicability and scalability in real-world medical domains.  
However, this study uses a relatively small dataset, emphasizing the necessity for heterogeneous data to augment the efficacy of its conclusions. Nevertheless, the sustainable lifecycle operation of this resolution facilitates a gradual enhancement in model efficacy through the involvement of various institutions and data aggregation, which will progressively alleviate these constraints throughout the duration of its execution.