FL holds an excellent promise in healthcare applications to improve medical services for both institutions and patients--for instance, predict autism spectrum disorder [18], mortality and intensive care unit (ICU) stay-time prediction [19], wearable healthcare devices [20; 21], and brain tumor segmentation [22]. However, FL algorithms face several challenges, mainly due to the properties of medical data, such as:  
* **Data partitions:** FL technique aims to solve the limited sample size problem for training a secure collaborative machine learning model by aggregating a group of clients' data. However, choosing a data partition (horizontal or vertical) for FL is essential to solve the limited sample size, limited sample features, or both. * **Data distribution (statistical challenge):** In developing a machine learning model in a centralized manner, the training data are centrally stored and balanced during training. However, with federated learning, each client generated the training data locally, remained decentralized, and cannot access the other clients' data. Thus, data distribution at one client can differ significantly from others, i.e., nonindependent and identically distributed (non-IID), impacting the performance of the federated learning model [23; 24]. * **Privacy and security**: Data privacy and security are critical issues in medical applications. It is impossible to assume all of the clients in FL are reliable because the number of clients expected to participate is potentially thousands or millions. Thus, privacy-preserving mechanisms are needed to protect medical data from untrusted clients or third-party attackers. * **Benchmark medical dataset:** Medical dataset quantity and quality have often limited the development of a robust solution to the FL algorithm. For various research purposes, the dataset used in FL experiments could vary significantly. For instance, some datasets focus on medical image classification and segmentation performance while others focus on network communication performance. However, the benchmark datasets have not already been compiled, specifically for medical datasets. Thus, a trusted benchmark is necessary to evaluate the performance of the FL that uses multiple medical data sources. Finally, we provide a comprehensive list of relevant medical datasets for future research on this topic. Due to the ever-changing development in FL, several valuable studies on FL have been published in reputable publications from 2018 to 2021. Therefore, this paper aims to provide a recent review of federated learning in the medical domain. Specifically, this study describes the existing FL techniques related to solving the challenges inherent in medical data together with future research direction on FL for healthcare applications. This study differs from existing reviews. General descriptions of FL are given in [16; 17], while detailed discussions of recent challenges are presented in [25; 26], security analysis [27], and personalization techniques [28]. Resumes of FL applications in edge computing [29], wireless networks [30], and healthcare [31; 32] also have been published. However, none of the existing studies have explored the impact of medical data properties on the performance of FL in great detail. Moreover, it is necessary to provide a comprehensive overview related to benchmarking the FL in medical data. To fill the gap, this review presents a survey of FL from the perspective of data properties including data partitions, data distribution, data privacy, benchmarking, and its promising applications. After a brief introduction of FL in this study, the rest of this paper is structured as follows. Section 2 describes the research method to conduct this study. Furthermore, in Section 3, we provide the search results from existing publications. Section 4 discusses our findings in data partition, data distribution properties, data privacy threats and protections, benchmark medical dataset, and open challenges applied in federated learning for medical applications.