clinical institution's data. However, it does not work well when applied to data from a different institution or even across departments within the same institution. Simultaneously, training deep learning models in a centralized data lake [11], as depicted in Figure 1b, is infeasible because of patient privacy and government regulations related to clinical data. Thus, to increase both the diversity and quantity of training data is through the collaboration of several healthcare institution to create a single deep learning model while maintaining patient privacy and confidentially. Medical data are usually fragmented due to the complex nature of the medical system and processes. For instance, each medical institution may be able to access the medical data of their patients only. As protected health information (PHI), these medical data are only disclosed strictly regulated by law to third parties. The process of accessing and analyzing medical data is strictly regulated by laws and regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) [12]. In addition, with an increasing number of data breaches at healthcare organizations, the prominence of data security and privacy protection has become a global consensus. For instance, in the American Medical Collection Agency (AMCA) recent healthcare data breach, the perpetrators have access to medical data, financial information, and payment details, affecting 11.9 million patients [13]. As a result, many countries around the globe are enacting stricter legislations to protect data security. For example, the General Data Protection Regulation (GDPR) went into effect in 2018 by the European Union to ensure users' privacy while protecting their data [14]. Under this GDPR, business entities must clearly explain why they need user data access and offer them the right to withdraw or delete their data. Business entities violating the regulation would face severe penalties. Many similar actions have taken place in the United States and Taiwan to protect individuals' privacy and security. For instance, Taiwan's Personal Data Protection Act (PDPA) and Cyber Security Management Act, enacted in 2018, prohibit online business entities from leaking or tampering with personal data details that they obtain [15]. This regulation enforces the business activities following the obligations of legal data protection. On the one hand, establishing these regulations will contribute to a more civil society's growth. On the other hand, these regulations introduce new challenges to data transaction and collaboration procedures for multi-institutional collaboration to train a deep learning model. One recent approach to solving the problem of training a robust deep learning model from federated medical data while preserving patient privacy is federated learning  
Figure 1: Single-institution and collaborative learning: (**a**) single-institution learning: machine learning model trained and validated with single institution dataset; (**b**) collaborative learning: machine learning model trained and validated with medical data collected from external institutions pooled in a central data lake. (FL) [16; 17]. This method provides decentralized machine learning model training without transmitting medical data through a coordinated central aggregate server. Medical institutions, working as client nodes, train their deep learning models locally and then periodically forward them to the aggregate server. The central server coordinates and aggregates the local models from each node to create a global model, then distributes the global model to all the other nodes. It is worth noting that the training data are kept private to each node and never transmitted during the training process. Only the model's weight and parameters are transmitted, ensuring that medical data remain confidential. For these reasons, FL mitigates many security concerns because it retains sensitive and private data while enabling multiple medical institutions to work together.