## 4 Discussion  
The aim of this section is to provide a performance comparison of the proposed audio-visual emotion recognition model accompanied by federated learning with other available state-of-the-art methods described in the literature and analyzed for the eNTERFACE'05 dataset. As already mentioned, there is a lack of multimodal audiovisual emotion recognition models that exploit federated learning. Consequently, similar experiments and dataset partitioning into such clients in the case of audiovisual emotion recognition are not described in the literature, which makes a comparison challenging. However, as the average performance per client in the case of a federated learning-based approach should not exceed the performance of the same non-federated model trained on the whole dataset, we compare average results with the performance of other non-federated learning-based methods available in the literature. The results are shown in Table 8. To highlight the importance of introducing federated learning techniques, we also provided the averaged results obtained by training the model for each client separately in a non-federated manner. By observing the results of the proposed model, we can conclude that the federated learning implementation increased multimodal accuracy by about 2% on average, whereas its influence is much more emphasized in the case of the audio modality. The performance of several state-of-the-art methods, tested in a non-federated manner in the case of the eNTERFACE'05 dataset is also provided. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**AVER**} & \multicolumn{3}{c}{**AVER + FL**} \\ \hline  & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline Client 1 & 0.90 & 0.89 & 0.89 & 0.94 & 0.93 & 0.93 \\ \hline Client 2 & 0.88 & 0.87 & 0.87 & 0.93 & 0.93 & 0.93 \\ \hline Client 3 & 0.93 & 0.93 & 0.93 & 0.95 & 0.94 & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Detailed average performance of the proposed AVER model. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Classification Accuracy (\%)**} \\ \hline
**Model** & **Audio Modality** & **Video Modality** & **Multimodal** \\ \hline CNN + LSTM [15] & 58.95 & 83.21 & 85.43 \\ \hline DL + attention [16] & / & / & 88.11 \\ \hline MERML on AV [17] & 55.9 & 77 & 91.50 \\ \hline C-GCN [18] & / & / & 97.07 \\ \hline AV + MoBEL [19] & 67.7 & 62 & 81.70 \\ \hline AVER & 58.33 & 88.49 & 91.27 \\ \hline AVER + FL & 67.86 & / & 93.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance of the state-of-the-art audiovisual emotion recognition methods. The methods [15; 16; 17; 18; 19] are designed using different approaches compared to the proposed method. By analyzing the performance of methods [15; 16; 17; 19], and by comparing them with the results of the proposed model, we can notice that the proposed method provides better multimodal accuracy. Also, the proposed method achieves better classification accuracy in the case of unimodal recognition compared to models [15; 17; 19]. Although the model given in [18] outperforms the proposed model in terms of the average classification accuracy, these observations should be considered carefully, as the performance of the proposed model is obtained by averaging the performance of each client after the federated learning procedure, unlike the procedure performed for other listed methods. In order to observe the achieved performance in detail, we provide a confusion matrix of the proposed AVER + FL model in Figure 4.  
By observing the results from Figure 4 and comparing them to the results from Figure 9 of the C-GCN model [18], we can observe that the proposed model provides better performance in the case of disgust and happiness, whereas the model from [18] outperforms the proposed model for other emotions.