## 4 Discussion
### Open Challenges  
In this survey, we review the current progress on federated learning in the healthcare field. We highlight the comprehensive solutions to federated learning issues related to medical data configurations to provide a valuable resource for researchers. In what follows, we list some potential research directions or open questions when federated learning is applied in the healthcare area. **FL with Medical Data Stream. ** Medical data streams are collections of medical data that increase constantly and rapidly over time, generated during the treatment and monitoring of patients. For instance, in telemedicine or patient monitoring, the medical monitoring devices generate a large amount of time-sensitive data when monitoring patient vital signs such as temperature, heart rate, and blood pressure. This medical data is a stream of medical signals displayed for interpretation by physicians. Certain pieces of these data could be used in real-time to alert physicians about changes in patient circumstances. Medical data streams arrive periodically, and we would like to develop an analytic model that extracts meaningful patterns or risk factors in real-time. Federated learning incorporated with the medical data stream could improve training tasks and security performance, as inconsistencies in evolving medical datasets and the data transmission between the FL coordinator and participant nodes can be highly decreased [25]. However, the medical data streams are usually fast, large, and we must handle them in real-time. In addition, the medical data streams are dynamic, so our FL algorithm has to respond to these changes. Thus, it is essential to design an efficient federated learning algorithm to achieve good accuracy, low total memory, and minimum time in medical data streams. **FL with Hybrid Medical Data Partition. ** In the HFL data partition, the nodes share the same features \(\mathcal{X}\) and label \(\mathcal{Y}\) but have different data samples \(\mathcal{I}\). Thus, the HFL aims to solve limited sample size variability by combining data samples from all nodes when developing a model, while for the VFL data partition the nodes share the same data samples \(\mathcal{I}\) but have different features \(\mathcal{X}\) and labels \(\mathcal{Y}\). Therefore, the VFL aims to enrich the features by combining features from all nodes when developing a model. However, we need to simultaneously solve a limited sample size variability and enrich the features when developing a model in practice. For instance, a healthcare node may possess either partial features or data samples in healthcare insurance, which serves only a fraction of users and only has partial records. Incorporating both the HFL and the VFL data partition will result in a hybrid data partition. Compared to the HFL and the VFL, a hybrid FL data partition has its challenges. In HFL, each node shares neither its local data nor labels. In contrast, in VFL, the node shares the user's index to the server or is securely stored in one node as a key for aligning the features [56]. A hybrid FL data partition needs to deal with both types of nodes, so the FL training algorithm can run without requiring the aggregate server to access any data, including the users' index. New architecture and training algorithms in FL will be required to utilize the benefits of the hybrid data partition effectively. **FL with Incentive Mechanism for Good Data Contributor. ** The internet of health things (IoHT) uses internet of things (IoT) devices on e-health applications that enable the connection between healthcare resources and patients. The IoHT devices such as smartwatches and healthcare wearable trackers can record heart rate, body temperature, and blood pressure. These rich healthcare data are excellent for personal smartphone healthcare apps that can run on device federated learning. However, the IoHT nodes are burdened by significant computation and communication costs during the federated model training process.