## 4 Tt-Vfd-Sim
### TT-VFDL-SIM for Classification
These experimental results also support that similar feature spaces contribute to the performance improvement of TT-VFDL-SIM. However, in TT-VFDL-SIM for the classification target task, DR tended to show relatively high performance for ACCHAR and RR for TRAFFIC. In addition to differences in domains, the two datasets also had differences in correlation coefficient values between input variables. In TRAFFIC, the correlation coefficient between each input variable was very high at 0.82, but in ACCHAR, the correlation coefficient between each input variable was very low at 0.04. In the regression target task, the correlation coefficients between the input variables of AIRQUAL, HOMENV, and GASVOLT were 0.88, 0.17, and 0.96, respectively. But it was advantageous to introduce a similar feature space based on decoded data in most experimental units. Therefore, it is still difficult to conclude that the lower the correlation coefficient between input variables in each dataset, the more advantageous it is to introduce a similar feature space based on decoded data. As a result, TT-VFDL-SIM was designed based on strategies of a task-driven transfer approach suitable for the characteristics of multivariate IoT time-series analysis, greatly improving performance and significantly reducing the communication amounts required for distributed learning. In particular, the strategy of leveraging task-driven features extracted for univariate partial tasks for merging task training and the strategy of introducing similar feature spaces for security and overfitting prevention were a great help to improve the performance of TT-VFDL-SIM. Ultimately, the task-driven transfer approach combines the advantages of communication efficiency from transfer learning-based approaches with the performance enhancement benefits of task-driven features. This makes the TT-VFDL-SIM a suitable VFDL design technique for multivariate IoT time-series analysis. ### Detailed Analysis for the TT-VFDL-SIM

In this section, the operational principles of each step of TT-VFDL-SIM which was designed leveraging the task-driven transfer approach are experimentally validated. These are based on the detailed experimental results of applying TT-VFDL-SIM to 45 experimental units distinguished into (FM Structure, Dataset, Compression Ratio). Additionally, based on this analysis, we summarize the contribution of the transfer learning mechanism in the task-driven transfer approach. The contents to be analyzed in this section are summarized as follows:

* Impact of similarity between similar feature spaces on the performance of TT-VFDL-SIM. * Experimental verification of the effect of introducing similar feature spaces using the SIM Partial Training mechanism of TT-VFDL-SIM. * Learning rate setting problem for merging task training in TT-VFDL-SIM. Relationship between partial task training performance and merging task training performance in TT-VFDL-SIM. * Contribution of transfer learning mechanism in task-driven transfer approach. #### 5.4.1 Experimental Results

For the regression target task, the detailed experimental results of applying TT-VFDL-SIM with the method option RD are summarized in Table 11, and the detailed experimental results of applying TT-VFDL-SIM with the method option DD are summarized in Table 12. The experiment was conducted on experimental units (FM Structure, Dataset, Compression Ratio). Compression Ratio is used to distinguish the reconstruction performance of decoded data for each dataset and is already summarized in Table 7. The SIM Partial Training (method option) column summarizes the average performance of SIM Partial Training in three guest parties. The Merging Task Training column summarizes the performance of merging task training in the host party, which is the performance when the target task is trained with TT-VFDL-SIM.