## 2 Related Works
### Previous Work
#### 2.4.2 The Previous Method
In classification tasks, the final performances of the proposed TT-VFDL-ST were higher than the maximum performances of the guest party for all cases. In regression tasks, the final performances of the proposed TT-VFDL-ST were higher than the maximum performances of the guest party for all cases except three cases. However, both in classification and regression tasks, the final performances were higher than the average performances of all guest parties for all cases. Therefore, the average performance of all guest parties might be used as an empirical lower bound to expect the final training quality. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{\begin{tabular}{c} **FM** \\ **Type** \\ \end{tabular} } & \multirow{3}{*}{**Dataset**} & \multirow{3}{*}{\begin{tabular}{c} **MSE Between** \\ **Decoded and** \\ **Raw Data** \\ \end{tabular} } & \multicolumn{3}{c}{**Per-Training (DD)**} & \multicolumn{3}{c}{**Fine-Tuning (RD)**} & \multirow{3}{*}{**MSE**} & \multirow{3}{*}{\begin{tabular}{c} **Rest** \\ **Learning** \\ **Rate** \\ \end{tabular} } \\ \cline{3-3} \cline{5-10}  & & & & **MAX** & & **MIN** & & **MAX** & & **MIN** \\  & & & **MSE** & **MSE** & **MSE** & & & **Rate** \\ \hline \multirow{3}{*}{\begin{tabular}{c} **MFC** \\ **MLFM** \\ \end{tabular} } & AIR\_ & \multirow{3}{*}{0.00007} & AIR\_ & \multirow{3}{*}{0.000850} & AIR\_ & \multirow{3}{*}{0.00342} & AIR\_ & \multirow{3}{*}{0.00606} & AIR\_ & \multirow{3}{*}{0.00336} & AIR\_ & \multirow{3}{*}{0.76} & AIR\_ \\  & QUALITY & & & & & & & & \\ \cline{1-1} \cline{3-10}  & SENSING\_ & \multirow{3}{*}{0.00002} & AIR\_ & \multirow{3}{*}{0.00595} & AIR\_ & \multirow{3}{*}{0.00226} & AIR\_ & \multirow{3}{*}{0.00364} & AIR\_ & \multirow{3}{*}{0.59} & AIR\_ \\  & HMOME & & & & & & & & \\ \cline{1-1} \cline{3-10}  & GAS\_ & \multirow{3}{*}{0.00002} & AIR\_ & \multirow{3}{*}{0.00676} & AIR\_ & \multirow{3}{*}{0.00501} & AIR\_ & \multirow{3}{*}{0.00440} & AIR\_ & \multirow{3}{*}{0.76} & AIR\_ \\  & CHAMBER & & & & & & & & \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} **MLFM** \\ \end{tabular} } & AIR\_ & \multirow{3}{*}{0.00007} & AIR\_ & \multirow{3}{*}{0.00614} & AIR\_ & \multirow{3}{*}{0.00360} & AIR\_ & \multirow{3}{*}{0.00345} & AIR\_ & \multirow{3}{*}{0.77} & AIR\_ \\  & QUALITY & & & & & & & & \\ \cline{1-1} \cline{3-10}  & SENSING\_ & \multirow{3}{*}{0.00002} & AIR\_ & \multirow{3}{*}{0.00307} & AIR\_ & \multirow{3}{*}{0.0029} & AIR\_ & \multirow{3}{*}{0.00210} & AIR\_ & \multirow{3}{*}{0.50} & AIR\_ \\  & HMOME & & & & & & & & \\ \cline{1-1} \cline{3-10}  & GAS\_ & \multirow{3}{*}{0.00002} & AIR\_ & \multirow{3}{*}{0.00630} & AIR\_ & \multirow{3}{*}{0.00468} & AIR\_ & \multirow{3}{*}{0.00444} & AIR\_ & \multirow{3}{*}{0.00285} & AIR\_ \\  & CHAMBER & & & & & & & & \\ \hline \multirow{3}{*}{
\begin{tabular}{c} **SLFM** \\ \end{tabular} } & AIR\_ & \multirow{3}{*}{0.00007} & AIR\_ & \multirow{3}{*}{0.00626} & AIR\_ & \multirow{3}{*}{0.00357} & AIR\_ & \multirow{3}{*}{0.00606} & AIR\_ & \multirow{3}{*}{0.00331} & AIR\_ \\  & QUALITY & & & & & & & \\ \cline{1-1} \cline{3-10}  & SENSING\_ & \multirow{3}{*}{0.00002} & AIR\_ & \multirow{3}{*}{0.00557} & AIR\_ & \multirow{3}{*}{0.00227} & AIR\_ & \multirow{3}{*}{0.00293} & AIR\_ & \multirow{3}{*}{0.50} & AIR\_ \\ \cline{1-1} \cline{3-10}  & HMOME & & & & & & & & \\ \cline{1-1} \cline{3-10}  & GAS\_ & \multirow{3}{*}{0.00002} & AIR\_ & \multirow{3}{*}{0.00630} & AIR\_ & \multirow{3}{*}{0.00468} & AIR\_ & \multirow{3}{*}{0.00444} & AIR\_ & \multirow{3}{*}{0.00285} & AIR\_ \\ \cline{1-1} \cline{3-10}  & CHAMBER & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Experiment for the proposed TT-VFDL-ST in regression according to max and min guest party performances. Figure 11: Data distributions of each raw data and its decoded data of each input stream of three datasets (HUMAN_ACTS, LOOP_SENSOR, AIR_QUALITY).