## 4 Discussion
### FL Studies for Healthcare Applications
#### 4.6.1 Classification Task in FL for Healthcare Applications
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ML Task** & **Clinical Tasks** & **Medical Input Data** & **Model Architecture** & **FL Study** \\ \hline \multirow{8}{*}{Classification} & Autism spectrum disorders & \multirow{2}{*}{fMRI} & \multirow{2}{*}{CNN} & \multirow{2}{*}{[18]} \\  & (ASD) or Healthy control (HC) & & & & \\ \cline{3-5}  & \multicolumn{4}{c}{Cancer diagnosis:} & & \\ \cline{3-5}  & & - & MRI & & \\ \cline{3-5}  & Prostate cancer & - & Ultrasound & - & GAN & [43] \\ - & Thyroid cancer & & images & - & CNN & [41] \\ - & Lung cancer & - & Tobacco and & - & MLP & [49] \\ \cline{3-5}  & & & radon data & & \\ \cline{3-5}  & COVID-19 detection & \multirow{2}{*}{X-ray images} & \multirow{2}{*}{CNN} & \multirow{2}{*}{[40; 44; 48; 54]} \\ \cline{3-5}  & Human activity & & Wearable device & LSTM & [20; 21; 45] \\ \cline{3-5}  & Human emotion & & Wearable device & CNN & [37; 42] \\ \cline{3-5}  & Patient hospitalization & \multirow{2}{*}{Patient EHR} & \multirow{2}{*}{SVM} & \multirow{2}{*}{[52]} \\ \cline{3-5}  & Patient mortality & & & & \\ \cline{3-5}  & Sepsis disease & \multirow{2}{*}{Patient EHR} & Double Deep Q & \\  & & & Network & [51] \\ \hline \multirow{4}{*}{Segmentation} & Brain tumor & MRI & U-Net & [38] \\ \cline{3-5}  & COVID-19 region & 3D Chest CT & 3D U-Net & [53] \\ \cline{1-1} \cline{2-5}  & Prostate cancer & \multirow{2}{*}{MRI} & \multirow{2}{*}{3D Anisotropic Hybrid Network} & [50] \\ \hline \hline \end{tabular}  
* CNN: convolutional neural network; CT: computed tomography; EHR: electronic health record; fMRI: functional magnetic resonance imaging; GAN: generative adversarial network; MLP: multilayer perceptron; MRI: magnetic resonance imaging; SVM: support vector machine.  
\end{table} 
Table 5: Summary of FL publications applied in medical applications. the challenge of lacking COVID-19 medical training data in a federated environment. The GAN-based architecture was employed in the proposed system and achieved a comparable performance of 94.11% accuracy. **Human activity and emotion recognition**. With increasing research on wearable technology and the internet of health things (IoHT), FL technology is one of the solutions to keep users' privacy while collaborating to develop a model for human activity and emotion recognition [20, 21, 37, 42, 45]. For example, [PERSON] et al. [20] developed a deep learning model for human activity classification such as walking, sitting, standing, and laying. Then the author elaborates the trained CNN-based model with federated transfer learning to achieve a personalization model for each edge device. The system achieved 99.4% accuracy in classifying human activities. Similarly, [PERSON] et al. [21] developed a cloud-edge federated learning infrastructure to create a patient privacy-aware deep learning model for in-home monitoring applications. The authors developed an autoencoder (AE) model architecture then deployed the model into five different healthcare nodes. The FL system achieved an accuracy of 95.41%. [PERSON] et al. [37] combined the speech signal and facial expression to create an emotion index for monitoring the patient's mental health. Using the facial emotion recognition (FER) dataset collected from several data silos, the author employed a federated learning technique and AE-based architecture to create a secure machine learning model to classify a human emotion. The FL algorithm showed an AUC of 88%. **Patient mortality prediction**. Similarly, FL enables early predictive modeling based on several sources, which can help to assist clinicians with extra information into the risks and benefits of treating patients earlier [19, 46, 47, 51, 52, 55, 56]. [PERSON] et al. [19] used drug features to forecast critical care patients' mortality, and ICU stays time. Their algorithm based on AE architecture also addresses non-IID ICU patient data by grouping patients into clinically significant communities with shared diagnoses and geographical regions, then training one model per community.