## 4 Tt-Vfd-Sim
### TT-VFDL-SIM for Classification
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **FM** \\ **Structure** \\ \end{tabular} } & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Metric**} & \multicolumn{2}{c}{**TT-VFDL-SIM \({}^{*}\)**} & \multirow{2}{*}{**DT-VFDL-AE**} & \multirow{2}{*}{**P-VFDL-SL**} & \multirow{2}{*}{**P-VFDL-SL**} & \multirow{2}{*}{**CINTR**} \\ \cline{3-3} \cline{6-9}  & & **(RR)** & & & & \\ \hline \multirow{8}{*}{MFCMLFM} & \multirow{2}{*}{ACCHAR} & \multirow{2}{*}{**Accuracy**} & **98.80** & **95.80** & 85.54 & 95.18 & 95.18 & 96.39 \\ \cline{2-9}  & & **F1-Score** & **0.99** & **0.99** & 0.86 & 0.95 & 0.95 & 0.96 \\ \cline{2-9}  & & **Accuracy** & 95.76 & 94.92 & 85.59 & 95.76 & **96.61** & 96.61 \\ \cline{2-9}  & & **F1-Score** & 0.96 & 0.94 & 0.85 & 0.96 & **0.97** & 0.97 \\ \hline \multirow{8}{*}{MLFM} & \multirow{2}{*}{ACCHAR} & Accuracy & **97.59** & **98.80** & 72.29 & 95.18 & **97.59** & 96.39 \\ \cline{2-9}  & & **F1-Score** & 0.98 & **0.99** & 0.55 & 0.95 & **0.98** & 0.96 \\ \cline{2-9}  & & **Accuracy** & **98.31** & **95.76** & 85.59 & **95.76** & **97.46** & 94.92 \\ \cline{2-9}  & & **F1-Score** & **0.98** & **0.96** & 0.87 & **0.96** & **0.97** & 0.95 \\ \hline \multirow{8}{*}{SLFM} & \multirow{2}{*}{ACCHAR} & Accuracy & **98.80** & **100.00** & 66.27 & **95.18** & 93.98 & 95.18 \\ \cline{2-9}  & & **F1-Score** & 0.99 & **1.00** & NaN & **0.95** & 0.95 & 0.95 \\ \cline{1-1} \cline{2-9}  & & **Accuracy** & **97.46** & **96.61** & 72.03 & **95.76** & **95.76** & 94.07 \\ \cline{1-1} \cline{2-9}  & & **F1-Score** & **0.97** & 0.96 & 0.64 & **0.96** & **0.96** & 0.94 \\ \hline \hline \end{tabular}

* \({}^{*}\)The proposed method.

\end{table} 
Table 9: Best performance of the proposed method and other comparison methods for the classification task. same experimental unit, the number of epochs for each method is different. However, each method was trained with the number of epochs that can achieve optimal performance through early stopping. Therefore, the communication amount summarized in Table 10 means the communication amount required for optimal performance of each method. For the same experimental unit, the communication amount of TT-VFDL-SIM and DT-VFDL-AE which take the transfer learning-based approach were noticeably smaller than the communication amount of P-VFDL-SL which takes the training loop parallelization approach. For the same experimental unit, TT-VFDL-SIM could reduce the communication amount required for optimal performance by at least 33.79 times and up to 721.87 times compared to P-VFDL-SL. Even the communication amount occurring in one step of each epoch of P-VFDL-SL was larger than the total communication amount of TT-VFDL-SIM or DT-VFDL-AE. This is because of gradients transmitted to each guest party in back propagation. The reason why TT-VFDL-SIM requires a slightly larger communication amount than DT-VFDL-AE is because communication occurs for the generation of similar feature spaces in the preparation stage. #### 5.3.2 Discussion

The experimental results in Section 5.3.1 support that the task-driven transfer approach, which uses transfer learning based on task-driven features, is more suitable for multivariate IoT time-series analysis than existing VFDL design approaches. This can be explained as due to the characteristics of the multivariate IoT time-series domain. In the multivariate IoT time-series analysis scenario, each variable is information that reflects the specific state in which the task is defined. Therefore, in multivariate IoT time-series analysis, it may be suitable to leverage task-driven features rather than data-driven features. DT-VFDL-AE leverages data-driven features, while CNTR, P-VFDL-SL, and TT-VFDL-SIM leverage task-driven features. As a result, DT-VFDL-AE had relatively lower performance than CNTR, P-VFDL-SL, and TT-VFDL-SIM. This experimentally supports that task-driven features are more suitable than data-driven features in multivariate IoT time-series analysis.