## 3 Task-Driven Transfer Approach
### Partial Task Construction
#### 3.1.2 LSTM-AE-Based Data Augmentation Technique  
In order to train each partial task model structure derived through the DFS Model Split technique, input and output must be assigned. For this purpose, we propose the LSTM-AE-based Data Augmentation technique. An overview of the LSTM-AE-based Data Augmentation technique is shown in Figure 5. The raw data of each guest party and host party are reconstructed based on LSTM-AE, and these reconstructed data (decoded data) are used for the input and output of each partial task model. The purposes of introducing decoded data into the input and output of the partial task model are as follows:  
1. When training a regression target task based on a completely distributed architecture, output data to train the partial task of each guest party are prepared without exposing the host party's raw data. 2. By introducing similar feature spaces generated from raw data and decoded data into partial task training, overfitting of the univariate partial tasks that pre-train the multivariate target task is prevented. TT-VFDL-SIM requires output data to train the partial task of each guest party. In the classification target task, the host party's raw output data, that is, encoded label data, can be transmitted to each guest party. However, due to security issues, the raw data of the host party cannot be used for the partial task training of each guest party in the regression target task. In the regression target task, the partial task of each guest party can be trained without  
Figure 5: LSTM-AE-based Data Augmentation technique to generate similar feature spaces. It is assumed that each guest party and host party configure both the encoder and decoder of LSTM-AE as a single LSTM layer. exposing the host party's raw data by leveraging the LSTM-AE-based Data Augmentation technique. The host party trains an LSTM-AE that can reconstruct its raw output data, as shown in Figure 5. Then, based on the trained LSTM-AE, an encoded latent vector dataset for the raw output data is generated and sent to each guest party along with the decoder parameters of the LSTM-AE. Each guest party constructs a decoder based on the parameters received from the host party and applies it to the encoded latent vector dataset. As a result, each guest party can generate decoded data for the host party's raw output data and use this as output for partial task training. Meanwhile, each guest party can use decoded data generated from its own raw data as input for partial task training. This is unrelated to the exposure issue of raw data for each guest party but rather intended to create similar feature spaces. The output of each partial task model is determined as either the host party's encoded label data or decoded data depending on the type of target task. Therefore, in one partial task model, two similar feature spaces can be defined depending on the type of input data, that is, raw data or decoded data. In this paper, the decoded data for the host party's raw data Y in the regression target task are denoted as YD. Meanwhile, in the classification target task, there is no need to generate LSTM-AE-based decoded data for Y, so the host party's YD is not defined. Additionally, the decoded data for each guest party's raw data X are denoted as XD. These notations are also summarized in Table 2 and used throughout this paper, including Tables 3 and 4 and Figures 3 and 5. In addition, to indicate the type of input/output data for training a partial task model, that is, the type of feature space, two uppercase letters are sometimes concatenated as shown in Table 4. The first capital letter indicates the type of input data, and the second capital letter indicates the type of output data. Additionally, R stands for raw data and D stands for decoded data. To easily distinguish between the types of feature spaces, R and D are marked in blue and red colors, respectively. * The proposed TT-VFDL-SIM method with various method options.