## 1 Introduction  
Currently, the advancement of artificial intelligence (AI) technology within the medical field is progressing rapidly, with several new AI technologies approaching feasibility [1, 2]. Extensive data accumulated within these sectors are essential for developing AI technologies. Nevertheless, the prevailing legal frameworks and regulations about data protection impose considerable limitations on the direct acquisition and dissemination of distributed data and computational resources [3, 4]. Consequently, a substantial portion of the data collected across diverse industries and research domains remains underexploited, thereby presenting a formidable challenge for the advancement of AI research and development [5]. Federated learning (FL), a sophisticated machine-learning paradigm wherein each participant in the process trains the model locally without the necessity of data aggregation, has emerged as a prominent methodology for training AI models while addressing the imperative of data privacy concerns among various organizations [5]. Hence, federated learning facilitates collaborative AI training among institutions without necessitating data sharing and is currently attracting considerable scholarly attention. This is receiving significant attention today, particularly in the medical domain, where federated learning is actively being used to address data-sharing restrictions and efficiently utilize distributed data for artificial intelligence research [6; 7; 8]. This study provides a comprehensive framework for implementing federated learning in AI applications in the medical domain. AI applications in the medical domain encounter numerous obstacles when deployed in practical environments. (1) Most AI models utilized in medicine are developed using limited datasets, primarily because of the limited availability of data within the medical domain. Given that data sourced from contexts divergent from the training environment are frequently non-independent and identically distributed (IID), AI solutions must be validated within these varied settings. (2) The full utilization of data gathered from multiple hospitals within the medical system is challenging. Stringent data regulations and the importance of privacy protection within the medical domain impose considerable restrictions on applying aggregated data from diverse hospitals and institutions to AI solutions. (3) AI models require continuous updates using new data. The data produced within the environments in which these solutions are operational must also be integrated into the training process of the AI model. To address these concerns, this study is establishing an environment in which diverse institutions can operate an AI solution via federated learning, enabling each institution to utilize its data proficiently. To construct this model, it is necessary to oversee the lifecycle of federated learning, including building models, aggregation, testing, evaluation, versioning, and deployment. Throughout the federated learning lifecycle, a global model was deployed to clients and the global model was retrained using data collected from each institution. The enhanced global model undergoes performance assessment and testing before being redistributed to institutions, where it forecasts and learns from newly generated data. This guarantees the establishment of a sustainable lifecycle management environment for federated learning. This study constructed a sustainable framework that facilitates the application of federated learning across diverse AI models within the medical domain. Consequently, any AI model can be used to implement federated learning, and this methodology will evolve into a standard approach for the integration of federated learning in the medical field. This methodology is not restricted to specific procedures or processes within the healthcare domain. Instead, it offers a comprehensive and universal guide applicable across various medical environments, providing a practical contribution that sets it apart from previous studies.