## 2 Backgrounds
### Deep Learning-Based Multivariate IoT Time-Series Analysis
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Acronyms** & **Description** \\ \hline \multirow{6}{*}{Generalized} & FE Part & Submodel structure of the target task model to extract features for each separate input stream. \\ \cline{2-3}  & FM Part & Submodel structure to perform “Feature Merging” where all layers are dense layers. That is, a submodel structure of the target task model which concatenates each input’s features and trains the target task. It is also used as a merging task model of the host party. \\ \cline{2-3}  & SLFM & Acronym of “Single Layer Feature Merging”. The simplest FM part structure. It consists of only a single dense layer. \\ \cline{2-3}  & MLFM & Acronym of “Multi-Layer Feature Merging”. MLFM consists of multiple dense layers. The features of each input are concatenated at a single level in the first dense layer of MLFM. \\ \cline{2-3}  & MFCMLFM & Acronym of “Multi-Layer Feature Merging with Multi-layer Feature Concatenation”. \\ \cline{2-3}  & MFCMLFM consists of multiple dense layers like MLFM, but it concatenates features through multiple depths. \\ \cline{2-3}  & Split FM & Submodel structure of each univariate partial task model of each guest party. It consists of all or part of layers of the FM part of the target task model. \\ \hline \multirow{6}{*}{Proposed Method} & TT-VFDL & Task-driven transferred vertical federated deep learning. Vertical federated deep learning based on the proposed task-driven transfer approach. \\ \cline{2-3}  & TT-VFDL-SIM & Task-driven transferred vertical federated deep learning with the introduction of similar feature spaces. The proposed vertical federated deep learning method designed based on our task-driven transfer approach. \\ \cline{2-3}  & DFS Model Split & Model split technique of the proposed approach using DFS (Depth-First Search) traversal. \\ \cline{2-3}  & Technique & LSTM-AE-based Data Augmentation Technique & Data augmentation technique of the proposed approach using LSTM autoencoder. \\ \cline{2-3}  & Target Task & Multivariate IoT time-series analysis task which could be trained distributedly by several VFDL methods including the proposed method. \\ \cline{2-3}  & Partial Task & Univariate IoT time-series analysis task based on deep learning of each guest party. \\ \cline{2-3}  & Merging Task & Multivariate task based on deep learning of the host party. \\ \hline \multirow{6}{*}{Existing Mechanism or Comparison Methods} & DT-VFDL & Vertical federated deep learning designed based on data-driven transfer approach. \\ \cline{2-3}  & DT-VFDL-AE & Vertical federated deep learning method based on transfer learning mechanism using data-driven features trained by autoencoder. Existing studies of [3; 8; 9; 10] correspond to this. \\ \cline{2-3}  & Existing Mechanism or & P-VFDL & Vertical federated deep learning designed based on training loop parallelization approach. \\ \cline{2-3}  & P-VFDL-SL & Existing studies of [2; 11; 12; 13; 14; 15] correspond to this. \\ \cline{2-3}  & P-VFDL-SL & Vertical federated deep learning method of representative mechanism of training loop parallelization approach, which is Split Learning [2]. Existing studies of [11; 12; 13; 14; 15] are based on this mechanism. \\ \cline{2-3}  & P-VFDL-SLG & Vertical federated deep learning method based on the advanced Split Learning mechanism with local gradient update. Method applying existing study [16] to vertical federated deep learning of the target task model. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Acronyms and terms used in this paper. constitutes multivariate IoT time-series data. We call the submodel structure that extracts features for each input stream the \"FE part\". Generally, CNN, LSTM, and CNN-LSTM are used in the FE part [22]. After concatenating the features extracted through the FE part from each input stream, the target task is trained to reflect the combination information between each input. We call this process \"feature merging\", and the submodel structure for feature merging is called the \"FM part\".