## 4 Tt-Vfd-Sim
### TT-VFDL-SIM for Classification
The task-driven transfer approach derives univariate partial tasks from a multivariate target task based on the DFS Model Split technique to train task-driven features. In \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{\begin{tabular}{c} **FM** \\ **Structure** \\ \end{tabular} } & \multirow{3}{*}{**Dataset**} & \multirow{3}{*}{**Task-Driven Transfer**} & \multicolumn{2}{c}{**Data-Driven Transfer**} & \multicolumn{2}{c}{**Training Loop**} & \multicolumn{2}{c}{**Ratio between**} \\ \cline{3-8}  & & & **TT-VFDL-SIM \({}^{*}\)** & & **DT-VFDL-AE** & **P-VFDL-SL** & **P-VFDL-SL** & **P-VFDL-SL** \\ \cline{3-8}  & & & **Preparation** & **Total** & & & \\ \cline{3-8}  & & & **Communication Amount (Bytes)** & & & **DT-VFDL-AE** & **TT-VFDL-SIM \({}^{*}\)** \\ \hline \multirow{6}{*}{\begin{tabular}{c} MFCMLFM \\ \end{tabular} } & AIRQUAL & 15,360 & 257,280 & 241,920 & 30,373,056 & 125.55 & 118.05 \\ \cline{2-8}  & HOMENV & 15,360 & 257,280 & 241,920 & 19,248,768 & 79.57 & 74.82 \\ \cline{2-8}  & GASVOLT & 14,361 & 249,561 & 235,200 & 37,566,144 & 159.72 & 150.53 \\ \cline{2-8}  & ACCHAR & 4452 & 96,852 & 92,400 & 22,579,200 & 244.36 & 233.13 \\ \cline{2-8}  & TRAFFIC & 10,392 & 158,232 & 147,840 & 114,222,528 & 722.61 & 721.87 \\ \hline \multirow{6}{*}{\
\begin{tabular}{c} MLFM \\ \end{tabular} } & AIRQUAL & 15,360 & 257,280 & 241,920 & 29,248,128 & 120.90 & 113.68 \\ \cline{2-8}  & HOMENV & 15,360 & 257,280 & 241,920 & 8,692,992 & 35.93 & 33.79 \\ \cline{2-8}  & GASVOLT & 14,361 & 249,561 & 235,200 & 56,814,912 & 241.56 & 227.66 \\ \cline{2-8}  & ACCHAR & 4452 & 96,852 & 92,400 & 31,997,952 & 346.30 & 330.38 \\ \cline{2-8}  & TRAFFIC & 10,392 & 158,232 & 147,840 & 85,034,880 & 575.18 & 537.41 \\ \hline \multirow{6}{*}{
\begin{tabular}{c} SLFM \\ \end{tabular} } & AIRQUAL & 15,360 & 257,280 & 241,920 & 32,622,912 & 134.85 & 126.80 \\ \cline{2-8}  & HOMENV & 15,360 & 257,280 & 241,920 & 52,468,416 & 216.88 & 203.94 \\ \cline{1-1} \cline{2-8}  & GASVOLT & 14,361 & 249,561 & 235,200 & 69,543,936 & 295.68 & 278.67 \\ \cline{1-1} \cline{2-8}  & ACCHAR & 4452 & 96,852 & 92,400 & 21,805,056 & 235.99 & 225.14 \\ \cline{1-1} \cline{2-8}  & TRAFFIC & 10,392 & 158,232 & 147,840 & 81,817,344 & 553.42 & 517.07 \\ \hline \hline \end{tabular}

* The proposed method.

\end{table} 
Table 10: Communication amount. other words, the task-driven transfer approach trains a multivariate target task based on task-driven features optimized for univariate partial tasks. On the other hand, CNTR and P-VFDL-SL train task-driven features suitable for multivariate target tasks. The experimental results in Section 5.3.1 show that the task-driven transfer approach is more effective. The variables in the scenario of multivariate IoT time-series analysis are dependent on each other because they are created simultaneously from a specific state and are linearly or non-linearly related to each other. Therefore, in TT-VFDL-SIM, task-driven features suitable for each univariate partial task can also be used for multivariate target task training. Another strategy of the task-driven transfer approach is to introduce similar feature spaces to ensure basic security and prevent overfitting for univariate partial tasks. In TT-VFDL-SIM, RD and DD were introduced for the regression target task, and RR and DR were introduced for the classification target task. And because the performance of TT-VFDL-SIM was excellent for all similar feature spaces, it can be said that the introduction of similar feature spaces contributes to improving the performance of TT-VFDL-SIM. Additionally, comparing the method options of TT-VFDL-SIM, the performance of DD was better than RD in the regression target task, and the performance of DR and RR was similarly good in the classification target task. In other words, a similar feature space based on decoded data helps improve the overall performance of TT-VFDL-SIM than a similar feature space based on raw data.