## 2 Related Works
### Previous Work
#### 2.4.2 The Previous Method
In [34], not only the continuous exposure of each guest party's feature and the host party's gradients but also the copy of raw time-series data of the host party to each guest party for the outputof regression tasks occurs. The continuous exposure of gradients in [12; 13; 14; 15; 34] causes data leakage problems [52; 53]. Therefore, the mechanisms of the proposed TT-VFDL-ST and our previous work [7] are relatively secure compared to [12; 13; 14; 15; 34]. However, especially for the classification tasks, encoded label data of the host party is exposed in the proposed TT-VFDL-ST and our previous work [7]. Encoded label data are less sensitive compared to the raw time-series data. However, to achieve more robustness in privacy, solutions for exposed features, weight matrices, and encoded labels are required. One of the possible solutions for the exposure of encoded labels is introducing indirect values [54]. Also, encryption is the basic solution to preserve the privacy of exposed features, weight matrices, and encoded labels. Compared to [12; 13; 14; 15; 34] needs homomorphic encryption [55] or functional encryption [56], traditional data encryption methods are enough for the proposed TT-VFDL-ST or our previous work [7]. This is possible because all sent data of the proposed TT-VFDL-ST or our previous method [7] are not used for the calculation in the middle of the training loop. Also, the strategies of the proposed TT-VFDL-ST are easily applied to the blockchain-based IoT [3; 57; 58]. ## 4 Experiments and Discussion

We verified TT-VFDL-ST and also the self-transfer partial training mechanism through the following experiments. Section 4.1 briefly describes the overview of the settings and main results. Section 4.2 verifies the performance and communication costs of the proposed TT-VFDL-ST for each regression and classification task. Section 4.3 verifies the proposed TT-VFDL-ST from the perspective of real training times and convergence. Also, we verify the detailed operations of the self-transfer partial training mechanism in Section 4.4. ### Description of Experiments

#### 4.1.1 Datasets and Target Tasks

The same five datasets used in our previous work [7] were used for verification. All datasets consist of time-series data from various IoT domains. Each dataset was constructed from public data repositories, which were mainly used as the benchmark for various types of machine learning tasks. We selected five repositories [59; 60; 61; 62; 63], which are collected from real world. Applying proper preprocessing techniques to each repository, we constructed five datasets in Table 3.

AIR_QUALITY is a dataset measuring various concentrations of substances related to air pollution. We constructed a regression target task to predict the concentration of benzene from other substances. SENSING_HOME is a dataset measuring the indoor and outdoor environment of a house. We constructed a regression target task to predict the temperature of a room using temperatures of other rooms and outdoors. With GAS_CHAMBER, we constructed a regression target task to predict the voltage of a heater using streams measured from two gas sensors. Also, we constructed a classification target task for human activity recognition using accelerometer streams with HUMAN_ACTS. LOOP_SENSOR is used to construct a classification target task to recognize daily traffic patterns using loop sensor measurement streams. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Task Type** & **Train** & **Valid** & **Test** \\ \hline AIR\_QUALITY & Regression & 504 & 72 & 144 \\ \hline SENSING\_HOME & Regression & 504 & 72 & 144 \\ \hline GAS\_CHAMBER & Regression & 490 & 70 & 140 \\ \hline HUMAN\_ACTS & Classification & 168 & 24 & 83 \\ \hline LOOP\_SENSOR & Classification & 294 & 28 & 118 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Dataset description. Each target task model has three variate inputs and one output. Inputs are distributed on three guest parties of \"completely distributed architecture\".