## 4 Tt-Vfd-Sim
### TT-VFDL-SIM for Classification
The highest performance for each method is selected and organized as the main performance for 15 experimental units distinguished into (FM Structure, Dataset). Detailed experimental results for various detailed conditions of TT-VFDL-SIM will be summarized in Section 5.4. By comparing metric values of the main performance for each method, TT-VFDL-SIM and other comparison methods are macroscopically analyzed as follows:

* Compared to approaches of existing VFDL methods, experimental verification of the suitability of the task-driven transfer approach, which was used in designing the TT-VFDL-SIM, for the multivariate IoT time-series domain. * Experimental verification of the effectiveness of partial task construction of the task-driven transfer approach. * Experimental verification of the effectiveness of generating and introducing similar feature spaces in a task-driven transfer approach. #### 5.3.1 Experimental Results

Tables 8 and 9 summarize the main performances of TT-VFDL-SIM, DT-VFDL-AE, P-VFDL-SL, P-VFDL-SLG, and CNTR for the regression target task and classification target task, respectively. The main performance for each method was measured for each experimental unit of (FM Structure, Dataset). Among the methods applied to each experimental unit, the method with the highest performance is marked in red, and the method with the second highest performance is marked in blue. Methods that show the same or improved performance as centralized training (CNTR) are indicated in bold. For the regression target tasks in Table 8, both method options RD and DD of TT-VFDL-SIM showed higher performance than CNTR. In addition, TT-VFDL-SIM showed the highest performance for all experimental units. The method with the second highest performance was also TT-VFDL-SIM. In addition, method option DD of TT-VFDL-SIM showed higher performance than method option RD in all experimental units except two experimental units. DT-VFDL-AE showed higher performance than CNTR for only 4 experimental units. P-VFDL-SL showed higher performance than CNTR for only 3 experimental units. However, they all showed lower performance than TT-VFDL-SIM. The MSE of TT-VFDL-SIM decreased by up to 0.00181 compared to CNTR and decreased by up to 0.00188 and 0.01143 compared to P-VFDL-SL and DT-VFDL-AE, respectively. Regarding the classification target tasks in Table 9, both method options RR and DR of TT-VFDL-SIM showed higher performance than CNTR for all experimental units except one. In addition, TT-VFDL-SIM showed the highest performance for all experimental units except for the one experimental unit where CNTR showed the highest performance. The method with the second highest performance was also mostly TT-VFDL-SIM. In the classification target task, method option RR and method option DR of TT-VFDL-SIM showed similarly good performance. For some experimental units of the classification target task, CNTR or P-VFDL-SL showed the second highest performance. However, the performance of CNTR or P-VFDL-SL was not higher than that of TT-VFDL-SIM for the same experimental unit. DT-VFDL-AE performed lower than CNTR for all experimental units. P-VFDL-SL outperformed CNTR for some experimental units. The accuracy of TT-VFDL-SIM increased by up to 4.82% compared to CNTR and increased by up to 4.82% and 33.73% compared to P-VFDL-SL and DT-VFDL-AE, respectively. Table 10 compares the communication amount of TT-VFDL-SIM, DT-VFDL-AE, and P-VFDL-SL. The \"Ratio between P-VFDL-SL and T-VFDL\" column shows the ratio of the communication amount between P-VFDL-SL and TT-VFDL-SIM or DT-VFDL-AE. P-VFDL-SL takes the training loop parallelization approach and TT-VFDL-SIM and DT-VFDL-AE take the transfer learning-based approach. The case with the largest difference is marked in red, and the case with the smallest difference is marked in blue. As described in Section 5.1.5, early stopping was applied to training for each experimental unit. Therefore, even for the