## 2 Related Works
### Previous Work
#### 2.4.2 The Previous Method
\begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{2}{c}{**Centralized Training: BASE**} \\ \hline \multicolumn{2}{c}{**Dataset**} & \multicolumn{2}{c}{**Single Machine**} \\ \cline{2-3} \multicolumn{2}{c}{**Feature Extraction (FE) Part**} & \multicolumn{2}{c}{**Feature Merging (FM) Part**} \\ \hline \multirow{4}{*}{AIR\_QUALITY} & INPUT(24) & \multirow{4}{*}{IF MFCMLFM:} \\  & Conv1D(50, relu) & 2 \(\times\) [DENSE(10, elu)] \\  & LSTM(10, tanh) & DENSE(1, linear) \\ \hline \multirow{4}{*}{SENSING\_HOME} & INPUT(72) & ELIF MLFM: \\  & LSTM(500, tanh) & DENSE(10, elu) \\  & DENSE(10, linear) & DENSE(1, linear) \\ \hline \multirow{4}{*}{GAS\_CHAMBER} & INPUT(70) & \multirow{4}{*}{ELIF.M.} \\  & 2 \(\times\) [Conv1D(140, elu)] LSTM(10, tanh) & \\ \hline \multirow{4}{*}{HUMAN\_ACTS} & INPUT(204) & \multirow{4}{*}{IF MFCMLFM:} \\  & Conv1D(50, relu) & & 2 \(\times\) [DENSE(10, elu)] \\  & Conv1D(50, elu) & & DENSE(class, linear) \\  & Flatten(0) & & ELIF MLFM: \\  & Dense(50, relu) & & DENSE(10, elu) \\  & Dense(10, relu) & & DENSE(class, linear) \\ \hline \multirow{4}{*}{LOOP\_SENSOR} & INPUT(144) & ELIF SLFM: \\  & 5 \(\times\) [Conv1D(50, elu)] & DENSE(class, linear) \\ \cline{1-1}  & LSTM(50, tanh) & & \\ \cline{1-1}  & DENSE(10, linear) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Structures of the target task model for each dataset when applying BASE (centralized training) method. #### 4.1.4 Overview of Results

For both regression and classification, overall performances were best in order of TT-VFDL-ST (proposed method), previous work [7], ref. [34], BASE, refs. [12; 13; 14; 15], refs. [8; 9; 10; 11], and CMP. Compared to [12; 13; 14; 15] taking parallelization for the training loop, TT-VFDL-ST and previous work [7] could achieve optimal performance while reducing data size for communication by up to 745.56 times and at least 19.31 times. Through these main results and other detailed results, we could experimentally verify the following:

1. **Verification of design approach for TT-VFDL-ST**
* The design approach for TT-VFDL-ST and also for our previous work [7] is effective in improving performance with low communication costs in the IoT domain. * In the IoT domain, features trained considering the aimed task (task-driven features) are more advantageous in terms of performance than features trained to reproduce each data (data-driven features). * In the IoT domain, task-driven features trained considering each univariate are more effective than those trained just considering multivariate. * Ultimately, the strategy of TT-VFDL-ST, which fine-tunes multivariate leveraging task-driven features obtained from each univariate by pre-training, is most effective in performance. 2. **Verification of self-transfer partial training mechanism of TT-VFDL-ST**
* The newly proposed self-transfer partial training mechanism could improve the performance of TT-VFDL-ST more than our previous work [7] by introducing similar feature spaces more effectively. * The main strategy of the self-transfer partial training, which is the transfer learning between two similar feature spaces with the same model structure, operates effectively. * Self-transfer partial training mechanism helps to prevent univariate partial tasks from being overfitted and to train task-driven features well suited for multivariate. 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. ### Performance Evaluation for TT-VFDL-ST

Tables 6 and 7 describe the results of applying each proposed method and existing methods for regression tasks. Also, Table 8 describes the results of applying each proposed method and existing methods for classification tasks. MSE and \(R^{2}\) are used for regression in Table 6. Table 7 organizes results using MSLE, RMSE, and MAE for regression. For classification metrics, accuracy, f1-score, precision, recall, sensitivity, and specificity are used.