## 2 Technologies in DTx
### Data Analysis
#### 2.2.2 Federated Learning for DTx  
Since digital therapeutics is a field that deals with sensitive medical data, it is required to implement a deidentification process for the collected user data. In particular, in a model training structure for machine learning, a method of collecting data at a single server site or institution is required. Data leakage, invasion, or security attacks will likely occur in this procedure. However, using a federated learning structure allows the researcher to be relieved of data security concerns. A federated learning (FL) algorithm does not collect data from users [33]. Instead, the central server generates one initial global model. Then, it distributes the model weights to the devices participating in the learning procedure, and each device learns the received model weights in its data. After that, the updated model weight is transmitted back to the central server from each user. The server finally integrates the collected model weights to create a new global model. This whole process repeats until the model converges. As seen from its structure, there is no movement of sensitive information from personal medical records when using federated learning. Therefore, the server is secure in terms of user data security or user data ownership because it cannot access or verify user information [34]. Various research studies have been conducted to use medical artificial intelligence technology that applies federated learning in multiple directions to solve the limitations of data regulations, such as the General Data Protection Regulation (GDPR) [35] and California's Privacy Rights Act (CPRA). For example, a study that applied a FL environment that classified pediatric chest radiographs based on patients' lung imaging data demonstrated that using the FL structure could lead to a similar performance compared to existing machine learning techniques while maintaining compliance with data security regulations. Medical federated learning research about diagnosing hypertrophic cardiomyopathy also showed a similar diagnostic performance to the machine learning algorithm for collecting sensitive medical datasets [36]. In addition to applying FL to medical image data, some studies adopt patient biosignal datasets to analyze their diseases. Representative research examples include classifying arrhythmia by applying federated learning into the CNN analysis of electrocardiogram data, the Fed-REMCS (Real-time Emotion State Classification from Multi-modal Streaming) study [37] that classified human emotions by analyzing physiological signals, and the PFCM (Personalized Federated Cluster Model) study [38] that analyzed depression severity through HRV analysis. The analysis results through medical AI are incorporated into digital therapeutics, and the system manages and treats the confirmed disease symptoms. However, there are still limitations to applying federated learning to digital therapeutics beyond the field of simple medical artificial intelligence research. Since most DTx products are mobile software, the first consideration must be understanding the different hardware devices used by each user. To apply digital therapeutics, it is necessary to use a hospital-to-patient structure for federated learning, as shown in Figure 3, among the structures presented in the previous research [39]. Yet, efficient data processing through machine learning becomes problematic since it uses a mobile platform. Recently, lightweight machine learning models such as MobileNets [40] have also been developed for limited-resource environments. However, it is still difficult to consider all the mobile user environments. In addition to resource limitations, issues such as client management, responsibility, and traceability still need to be solved in medical federated learning [41].