## 2 Related Works
### Previous Work
#### 2.4.2 The Previous Method
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **FM** \\ **Type** \\ \end{tabular} } & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Metric**} & \multicolumn{2}{c}{**Previous Work**} & \multicolumn{2}{c}{**Transfer**} & \multicolumn{2}{c}{**Parallelize**} \\  & & & **Proposed** & [7] & **Data-Driven Features** & & **Training Loop** & & \\ \cline{3-10}  & & & **TT-VFDL-ST** & **Option** & **Option** & **DR** & [8; 9; 10; 11] & **CMP** & [12; 13; 14; 15] & [34] & **BASE** \\ \hline \multirow{6}{*}{\begin{tabular}{c} MPC \\ MLFM \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} HUMAN- \\ ACTS \\ \end{tabular} } & TP & 83 & 82 & 82 & 71 & 64 & 79 & 79 & 80 \\ \cline{3-10}  & & FP & 0 & 1 & 1 & 12 & 16 & 4 & 4 & 3 \\ \cline{3-10}  & & TN & 249 & 248 & 248 & 237 & 233 & 245 & 245 & 246 \\ \cline{3-10}  & & FN & 0 & 1 & 1 & 12 & 19 & 4 & 4 & 3 \\ \cline{3-10}  & & TP & 118 & 113 & 111 & 100 & 102 & 113 & 114 & 114 \\ \cline{3-10}  & & FP & 0 & 5 & 6 & 16 & 15 & 5 & 4 & 3 \\ \cline{3-10}  & & TN & 708 & 703 & 702 & 692 & 693 & 703 & 704 & 705 \\ \cline{3-10}  & & FN & 0 & 5 & 7 & 18 & 16 & 5 & 4 & 4 \\ \hline \multirow{6}{*}{\begin{tabular}{c} MLFM \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} HUMAN- \\ ACTS \\ \end{tabular} } & TP & 83 & 81 & 82 & 35 & 62 & 79 & 81 & 80 \\ \cline{3-10}  & & FP & 0 & 1 & 1 & 9 & 17 & 4 & 2 & 3 \\ \cline{3-10}  & & TN & 249 & 248 & 248 & 240 & 232 & 245 & 247 & 246 \\ \cline{3-10}  & & FN & 0 & 2 & 1 & 48 & 21 & 4 & 2 & 3 \\ \cline{3-10}  & & TP & 116 & 115 & 113 & 101 & 91 & 113 & 115 & 110 \\ \cline{3-10}  & & FP & 2 & 2 & 4 & 12 & 18 & 5 & 3 & 3 \\ \cline{3-10}  & & FN & 706 & 704 & 696 & 690 & 703 & 705 & 705 \\ \cline{3-10}  & & FN & 2 & 3 & 5 & 17 & 27 & 5 & 3 & 8 \\ \hline \multirow{6}{*}{\
\begin{tabular}{c} SLFM \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} HUMAN- \\ ACTS \\ \end{tabular} } & TP & 83 & 82 & 83 & 0 & 19 & 79 & 78 & 79 \\ \cline{3-10}  & & FP & 0 & 1 & 0 & 0 & 9 & 4 & 4 & 4 \\ \cline{3-10}  & & TN & 249 & 248 & 249 & 249 & 240 & 245 & 245 & 245 \\ \cline{3-10}  & & FN & 0 & 1 & 0 & 83 & 64 & 4 & 5 & 4 \\ \cline{3-10}  & & TP & 117 & 115 & 113 & 57 & 47 & 113 & 113 & 110 \\ \cline{3-10}  & & FP & 1 & 3 & 4 & 3 & 8 & 4 & 5 & 6 \\ \cline{3-10}  & & TN & 707 & 705 & 704 & 705 & 700 & 704 & 703 & 702 \\ \cline{3-10}  & & FN & 1 & 3 & 5 & 61 & 71 & 5 & 5 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Evaluation of best performance for each dataset and target task model in classification using other metrics. With these results, we could verify the design approach for TT-VFDL-ST: That is, the design approach for TT-VFDL-ST, which was proposed in our previous work [7], is effective in improving performance with low communication costs in the IoT domain since TT-VFDL-ST shows the highest performance and our previous work [7] shows the next best performance. For one strategy of our approach, features trained considering the aimed task (task-driven features) are more advantageous for performance than features trained to reproduce each data (data-driven features) in the IoT domain. This is because what is a specific task is important in the analysis of the IoT domain. Therefore, refs. [8; 9; 10; 11] and CMP, which utilize data-driven features, show the lowest performance among all methods. All other methods including TT-VFDL-ST and BASE utilize task-driven features trained in their own way. For another strategy of our approach, task-driven features trained considering each univariate is more effective than trained just considering multivariate in the IoT domain. This is because, in multivariate data from the IoT domain, univariates are related to each other [41]. Therefore, TT-VFDL-ST and our previous work [7], which first considered only univariate and then fine-tuning for multivariate, show the highest performance. Ref.