## 4 Discussion
### FL Studies for Healthcare Applications
#### 4.6.1 Classification Task in FL for Healthcare Applications  
Classification is a common task tackled in the published FL applications in the medical domain. In machine learning, classification algorithms learn how to classify or annotate a given set of instances with classes or labels. There are several classification tasks that are studied in federated learning setting in healthcare, e.g., autism spectrum disorder (ASD) [18], cancer diagnosis [41; 49; 43], COVID-19 detection [40; 44; 48; 54], human activity and emotion recognition [20; 21; 37; 42; 45], patient hospitalization prediction [52], patient mortality prediction [19; 46; 47; 55; 56], and sepsis disease diagnosis [51]. The summary of classification tasks in FL studies for medical application is listed in Table 5.  
**Cancer diagnosis**. Recent studies show that researchers are employing FL technology to develop machine learning models for cancer diagnostic applications [41; 43; 49]. For instance, [PERSON] et al. [27] proposed a CNN-based model to classify whether thyroid nodules were benign or malign. The training data were 8457 ultrasound images collected from six institutions. The results show that the performance of the FL-based method was comparable with centralized learning with accuracy, sensitivity, and specificity of 97%, 98%, and 95%, respectively. Similarly, [PERSON] et al. [49] implemented FL with an MLP model for lung cancer classification using two independent cloud providers. The model initialized, trained, and transferred from one node to another node using a cloud repository. The model achieved 92.8% accuracy to classify cancer. Another study by [PERSON] et al. [43] transformed all nodes' raw medical image data onto a common space via image-to-image  
translation without violating FL's privacy settings. The image-to-image translation was done using a cycle generative adversarial network (CycleGAN) model. The performance of the proposed method trained with eight medical nodes achieved 98% accuracy and 99% area under the curve (AUC) to classify prostate cancer. **COVID-19 detection**. For COVID-19 detection applications [40; 44; 48; 54], FL is a potential approach for connecting medical images data from medical institutions, enabling them to develop a model while maintaining patient privacy. In this case, the model's performance is considerably enhanced from diverse medical datasets from several institutions. For instance, [PERSON] et al. [54] experimented with different federated learning architectures for binary COVID-19 classification. Their results showed that the federated learning model with GAN architecture and stochastic gradient descent (SGD) optimizer had a higher accuracy while keeping the loss score lower than the centralized machine learning model. The model performance achieved accuracy and AUC of 98.30% and 9.63%, respectively. Similarly, [PERSON] et al. [48] showed the efficacy of a federated learning system for detecting COVID-19-related CT anomalies using patients' medical data from one country hospital as training data, then validating the model with medical data from other countries. Specifically, the authors trained an MLP-model using 132 patients from three hospitals in Hong Kong and validated the model generalizability performance using the medical dataset from China and Germany. The system achieved 83.12% in terms of AUC. [PERSON] et al. [40] showed that increasing the number of medical nodes will decrease the training round for the model to converge and increase the model performance in CT-X-ray COVID-19 prediction. The authors proposed the CNN-based model architecture and achieved a performance of 95.27% AUC score. Similar results were obtained by [PERSON] et al. [44], who proposed an FL framework that enables medical nodes to generate high-quality training data samples with a privacy-protection approach. Specifically, the proposed method solves