## 6 Results and Discussion
\begin{table}
\begin{tabular}{c c} \hline \hline  & \begin{tabular}{c} \(\text{Conv1}\)\(\rightarrow\)relu\(\rightarrow\)pool\(\rightarrow\)Conv2\(\rightarrow\)relu\(\rightarrow\)pool\(\rightarrow\)Conv3\(\rightarrow\)relu \\ \(\rightarrow\)pool\(\rightarrow\)dropout\(\rightarrow\)fc1\(\rightarrow\)relu\(\rightarrow\)dropout\(\rightarrow\)fc2 \\ \end{tabular} \\ \
\begin{tabular}{c} **Model Architecture** \\ \end{tabular} &
\begin{tabular}{c} \(\text{Conv1}\): 16 filters, size 3 \(\times\) 3, padding = 1 \\ Conv2: 32 filters, size 3 \(\times\) 3, padding = 1 \\ Conv3: 64 filters, size 3 \(\times\) 3, padding = 1 \\ Pool: size = 2 \(\times\) 2, stride = 2 \\ dropout: (0.25) \\ fc1: linear layer (576 \(>\) 500) \\ fc2: linear layer (500, 27) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: MLP Architecture for the EMNIST-Letters datasets. violating privacy. The server then combines the raw data from the clients and uses it to train a global model. Table 6 describes the sizes of the datasets used in the experiments. As shown in Table 7, the CNN model achieved 99% accuracy on the MNIST test set, 91% on the Fashion-MNIST, and 93% on the EMNIST-Letters test set, while the MLP model achieved 98% on the MNIST test set, 89% on the Fashion-MNIST, and 89% on the EMNIST-Letters test set. Furthermore, Table 7 reports the training and testing time of the models on the different datasets. The training time for the CNN model ranged between 82 and 133 s, while for the MLP model, it ranged between 25.6 and 118 s.  
In general, this conventional collaborative scenario represents a server-based framework or direct collaborative deep learning [6]. In this approach, each user uploads a local dataset to the central server during training to train the deep learning model. Since this framework uses all training data, it is expected to achieve the best classification performance. However, it comes with privacy risks; this scenario is susceptible to direct privacy leakage [1] by exposing data to another party. Moreover, as it transfers all client data to the server, it may result in significant network overheard. In our privacy-preserving learning framework, the server gathers the encoded data sent by all clients and trains a global model. Table 8 displays the sizes and percentages relative to the full dataset sizes of the reduced encoded datasets, reduced by the ENN algorithm, and used to train and test privacy-preserving collaborative models. Table 9 shows that the MLP model in the proposed framework achieved 97% accuracy on the MNIST test dataset, down from 98%. It also reached 85% on the Fashion-MNIST dataset, a decrease from 89%, and 85% on the EMNIST-Letters test set, also down from 89%. Furthermore, Table 9 indicates that the CNN model in the proposed framework achieved 96% accuracy on the MNIST test set, down from 99% in the conventional collaborative framework. It also achieved 84% on the Fashion-MNIST, down from 91% in the conventional framework, and 82% on the EMNIST-Letters test set, down from 93%. The larger gap between the CNN and MLP models in the two frameworks may be attributed \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Training Data Size** & **Test Data Size** \\ \hline
**MNIST** & 60,000 & 10,000 \\
**Fashion-MNIST** & 60,000 & 10,000 \\
**EMNIST-Letters** & 145,600 & 20,800 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The sizes of the datasets used in the conventional collaborative models experiments.