## 5 Simulation and Experimental Evaluation
After the operation, the weights were sent to all devices. This solution is a classic approach to this type of problems. On the one hand, this solution does not load devices, only the server, but it forces access to the Internet and worsens the state of privacy when it comes to data. The server classifier can be trained with data regardless of the users of the product, or with data obtained from them. Both solutions are not the best ones, because the first may not be best suited to selected users, and the other one sends and saves private data outside the devices. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline  & & & \multicolumn{2}{c}{**A Pair I**} & \multicolumn{2}{c}{**A Pair II**} & \multicolumn{2}{c}{**A Pair III**} & \multicolumn{2}{c}{**A Pair IV**} & \multicolumn{2}{c}{**A Pair V**} & \multicolumn{2}{c}{**Average**} \\
**Device** & **Error** & **Priority** & **Reward** & **Time [h]** & **Reward** & **Time [h]** & **Reward** & **Time [h]** & **Reward** & **Time [h]** & **Reward** & **Time [h]** & **Reward** & **Time [h]** \\ \hline Smartphone & 0.01 & 3 & 150 & 74 & 73 & 150 & 1 & 74 & 1 & 87 & \\ Smartwatch & 0.01 & 3 & 71 & 150 & 1 & 150 & 72 & 150 & 1 & 98 & 1 \\ Smartphone & 0.001 & 2 & 48 & 5 & 100 & 6 & 100 & 4 & 100 & 7 & 49 & 6 & 66 & 6 \\ Smartwatch & 0.001 & 2 & 100 & 45 & 44 & 44 & 48 & 100 & 56 & 5 & \\ Smartphone & 0.0001 & 1 & 22 & 50 & 50 & 13 & 24 & 24 & 28 & 15 \\ Smartwatch & 0.0001 & 1 & 50 & 19 & 23 & 50 & 16 & 50 & 18 & 32 & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Obtained results for a network composed of 2 hidden layers. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**A Pair I**} & \multicolumn{2}{c}{**A Pair II**} & \multicolumn{2}{c}{**A Pair III**} & \multicolumn{2}{c}{**A Pair IV**} & \multicolumn{2}{c}{**A Pair V**} & \multicolumn{2}{c}{**Average**} \\
**Device** & **Error** & **Priority** & **Reward** & **Time [h]** & **Reward** & **Time [h]** & **Reward** & **Time [h]** & **Reward** & **Time [h]** & **Reward** & **Time [h]** & **Reward** & **Time [h]** \\ \hline Smartphone & 0.01 & 3 & 150 & 9 & 150 & 7 & 66 & 11 & 64 & 9 & 68 & 6 & 83 & 8 \\ Smartwatch & 0.01 & 3 & 55 & 9 & 58 & 7 & 150 & 11 & 150 & 9 & 150 & 6 & 93 & 8 \\ Smartphone & 0.001 & 2 & 100 & 45 & 27 & 37 & 33 & 39 & 36 & 100 & 34 & 54 & 31 \\ Smartwatch & 0.001 & 2 & 26 & 100 & 44 & 44 & 100 & 42 & 3 & 61 & \\ Smartphone & 0.0001 & 1 & 50 & 50 & 50 & 42 & 50 & 50 & 50 & 42 & 57 & 17 & 50 \\ Smartwatch & 0.0001 & 1 & 20 & 45 & 18 & 42 & 23 & 55 & 21 & 49 & 23 & 57 & 17 & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Obtained results for a network composed of 3 hidden layers. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**A Pair I**} & \multicolumn{2}{c}{**A Pair II**} & \multicolumn{2}{c}{**A Pair III**} & \multicolumn{2}{c}{**A Pair IV**} & \multicolumn{2}{c}{**A Pair V**} & \multicolumn{2}{c}{**Average**} \\
**Device** & **Error** & **Priority** & **Reward** & **Time [h]** & **Reward** & **Time [h]** & **Reward** & **Time [h]** & **Reward** & **Time [h]** & **Reward** & **Time [h]** \\ \hline Smartphone & 0.01 & 3 & 150 & 150 & 14 & 150 & 150 & 23 & 150 & 125 & 15 & 12 & 42 & 15 \\ Smartwatch & 0.01 & 3 & 45 & 11 & 54 & 39 & 16 & 57 & 23 & 61 & 12 & 42 & 15 \\ Smartphone & 0.001 & 2 & 100 & 45 & 48 & 42 & 100 & 44 & 100 & 55 & 43 & 65 & 47 \\ Smartwatch & 0.001 & 2 & 42 & 100 & 34 & 45 & 45 & 100 & 49 & 53 & 47 \\ Smartphone & 0.0001 & 1 & 50 & 50 & 50 & 50 & 50 & 50 & 23 & 37 & 37 & 131 \\ Smartwatch & 0.0001 & 1 & 18 & 123 & 22 & 117 & 12 & 15 & 137 & 50 & 145 & 19 & 131 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Obtained results for a network composed of 4 hidden layers. Despite all the disadvantages, we tested this solution to compare it with the one described in this paper. In Figure 5, there is a graph of time depending to error.