## 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}{*}{**FM Type**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{\begin{tabular}{c} **MSE Between** \\ **Decoded and** \\ **Raw Data** \\ \end{tabular} } & \multicolumn{4}{c}{**Self-Transfer Partial Training**} & \multicolumn{4}{c}{**Merging Task Training**} \\ \cline{3-10}  & & & \multicolumn{2}{c}{**Pre-Training (DR)**} & \multicolumn{2}{c}{**Fine-Tuning (RR)**} & \multicolumn{2}{c}{**Securacy**} & \multirow{2}{*}{\begin{tabular}{c} **Best** \\ **Learning** \\ **Rate** \\ \end{tabular} } \\ \cline{3-10}  & & & **Average** & **Average** & **Average** & **Average** & **Average** & **F1-Score** & & **1-Score** \\ \cline{3-10}  & & & **Acc. (\%)** & **F1-Score** & **Acc. (\%)** & **F1-Score** & & & **0.01** \\ \hline \multirow{4}{*}{MLFM} & \multirow{4}{*}{\
\begin{tabular}{c} **HUMAN-** \\ **ACTS** \\ \end{tabular} } & **0.00002** & **87.95** & **0.86** & **93.98** & **0.94** & **100.00** & **1.00** & **0.001** \\ \cline{2-10}  & & 0.00007 & 79.92 & 0.80 & 89.96 & 0.90 & **98.80** & **0.99** & 0.001 \\ \cline{2-10}  & & 0.00149 & 74.70 & 0.74 & 79.12 & 0.79 & **97.59** & **0.98** & 0.001 \\ \cline{2-10}  & & **0.00002** & **77.97** & **0.76** & **85.88** & **0.85** & **98.31** & **0.98** & **0.01** \\ \cline{2-10}  & & 0.00003 & 68.36 & 0.70 & 83.33 & 0.84 & **97.46** & **0.98** & 0.001 \\ \cline{2-10}  & & 0.00008 & 77.12 & 0.77 & 80.79 & 0.80 & **94.92** & **0.95** & 0.001 \\ \hline \multirow{4}{*}{SLFM} & \multirow{4}{*}{
\begin{tabular}{c} **HUMAN-** \\ **ACTS** \\ \end{tabular} } & **0.00002** & **83.94** & **0.83** & **94.38** & **0.94** & **100.00** & **1.00** & **0.001** \\ \cline{2-10}  & & 0.00007 & 80.72 & 0.81 & 87.55 & 0.88 & **98.80** & **0.99** & 0.001 \\ \cline{2-10}  & & 0.00149 & 69.48 & 0.70 & 84.34 & 0.83 & **97.59** & **0.98** & 0.001 \\ \cline{2-10}  & & **0.00002** & **76.56** & **0.76** & **84.74** & **0.87** & **99.15** & **0.99** & **0.001** \\ \cline{2-10}  & & 0.00003 & 71.19 & 0.72 & 85.31 & 0.85 & **98.31** & **0.98** & 0.01 \\ \cline{2-10}  & & 0.00008 & 70.34 & 0.71 & 83.62 & 0.84 & **96.61** & **0.97** & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: _Cont._Also, the performance in the host party of TT-VFDL-ST is higher than that of [7] even for some setups where TT-VFDL-ST showed lower average performance in guest parties than [7]. However, the linear correlation between the average performances of self-transfer partial training and the host party performance in TT-VFDL-ST was slightly lower than that of [7]. This is because, in TT-VFDL-ST, the performance in the host party, which is related to relatively low performance in guest parties, was improved since the self-transfer partial training mechanism prevents overfitting. As a result, two types of transfer learning in TT-VFDL-ST are both effective in performance improvement: one is for the aimed task and also for the partial task itself performed by the self-transfer partial training mechanism. Figure 11 shows the data distribution of raw data and decoded data of each stream in HUMAN_ACTS, LOOP_SENSOR, and AIR_QUALITY. In Figure 11, when the MSE between decoded data and raw data is smaller, the decoded data well preserved the statistical properties of its raw data. From Tables 13 and 14, better performance was achieved with decoded data preserving statistical properties. These results empirically support our design theory of the self-transfer partial training mechanism described in Section 3.2. The proposed TT-VFDL-ST is well performed since its self-transfer partial training mechanism is effective for improving performance when the source domain which well preserves the statistical properties of the target domain generated. Tables 15 and 16 summarize the minimum and maximum performance of the guest party with the final performance of the host party for regression and classification tasks, respectively. This is compared to Tables 13 and 14, which summarize the final performance with the average performance of all guest parties.