## 4 Tt-Vfd-Sim
### TT-VFDL-SIM for Classification
In the method option RR, task-driven features are extracted from the raw data (X) for each guest party based on the partial task model trained on their respective raw data (X). Meanwhile, in method option DR, one of two feature options is selected to extract task-driven features of each guest party (lines 22-25). Feature option DR(D) extracts features for the decoded data (XD) based on the partial task model trained on the decoded data (XD) of each guest party. On the other hand, feature option DR(R) extracts features for raw data (X) based on a partial task model trained on the decoded data (XD) of each guest party. Raw data (X) never appeared in the training dataset of the partial task model. In other words, feature option DR(R) of method option DR is a type of transfer learning. The host party fine-tunes the target task by training the merging task based on the task-driven features of guest parties (lines 27-36). Through the DFS Model Split technique, we derive the structure of the merging task model and allocate its inputs and outputs. The input is the task-driven features of each guest party, and the output is the raw data (Y) of the host party, that is, encoded label data. If the SIM Partial Training mechanism's method option is DR and the feature option is DR(D) when training the partial task model at each guest party, then the host party has an opportunity to apply feature option DR(D)-Test(R) to the trained merging task model (lines 35-36). In other words, task-driven features for X are extracted based on the partial task model trained on XD. And this can be applied to a merging task model trained based on task-driven features extracted from XD. The raw data (X) of each guest party never appear in the training process of the partial task model and merging task model. In other words, feature option DR(D)-Test(R) is a type of transfer learning. Additionally, feature option DR(D)-Test(R) is suitable for real-world scenarios. This is because by using X as input to a model trained and deployed based on XD, the overhead of creating XD for model input can be reduced. ## 5 Experiments and Discussion

In this section, we analyze the performance of TT-VFDL-SIM and validate the suitability of the task-driven transfer approach through experiments. In Section 5.2, prior to analyzing the performance of TT-VFDL-SIM, we summarize the results of applying the LSTM-AE-based Data Augmentation technique to each dataset to create similar feature spaces required for TT-VFDL-SIM. In other words, we summarize similar feature spaces created in the process of implementing TT-VFDL-SIM for the experiments in this section. Section 5.3 compares the representative performance metrics and communication amount of TT-VFDL-SIM and other comparison methods for the regression target task and classification target task, respectively. In Section 5.4, we validate the suitability of each stage that constitutes the task-driven transfer approach based on detailed experimental results for TT-VFDL-SIM. ### Overview of the Experiments

#### 5.1.1 Dataset Preparation

Table 5 is an overview of the five IoT time-series datasets used in the experiment. All five datasets are multivariate IoT time-series datasets consisting of four streams. For each dataset, a multivariate target task with three streams as input and one stream as output was constructed and used in the experiment. AIRQUAL, HOMENV, and GASVOLT are datasets for the regression target task, and ACCHAR and TRAFFIC are datasets for the classification target task. Five datasets were constructed from data sources donated by various data providers to be used as benchmarks for machine learning [36; 37]. The benchmark data in [36; 37] are the de facto official data used in time series research. We also selected data sources suitable for our research goals from [36; 37] and constructed five datasets for the experiment. In particular, all datasets selected for experiments are IoT time-series data collected in the real world.