## 3 Task-Driven Transfer Approach  
The target task of TT-VFDL-SIM is a multivariate task that accepts all raw data from each guest party as input and can be analyzed using a deep learning model with the structure shown in Figure 1. Each guest party in the completely distributed architecture stores IoT time-series data such as sensor streams. If the host party's data are IoT time-series data, the target task is a regression task. And if the host party's data are encoded label data, the target task is a classification task. The partial tasks derived from the target task are univariate tasks that have only data from each guest party as input. The host party's merging task is also derived from the target task and is a multivariate task that trains the target task using each guest party's task-driven features as input. TT-VFDL-SIM derives partial tasks from the target task, then reorganizes these partial tasks into pre-training and fine-tuning tasks for the target task and performs transfer learning on them to distributedly train the target task. We call this approach the \"task-driven transfer approach\". Since each variable in multivariate IoT time-series data is generally dependent on each other, the task-driven transfer approach leverages univariate partial tasks for the pre-training of multivariate target tasks. Each variable in multivariate IoT time-series data is created simultaneously in the process of monitoring a specific environment or condition. Therefore, we assumed that task-driven features optimized for univariate partial tasks would also be suitable for the multivariate target task. For similarreasons, it was assumed that task-driven features would be more suitable than data-driven features in multivariate IoT time-series analysis. In this section, we describe each stage of the task-driven transfer approach to design TT-VFDL-SIM. The task-driven transfer approach consists of the following four stages:  
1. **Partial Task Construction (Preparation)** - Derive partial tasks and merging task model structure (_DFS Model Split technique_) - Generate similar feature spaces (_LSTM-AE-based Data Augmentation technique_)
2. **Partial Task Training (Pre-training for the target task)** - Introduce similar feature spaces (_SIM Partial Training mechanism_)
3. **One-shot Communication** - Transfer task-driven features
4. **Merging Task Training (Fine-tuning for the target task)** - Train the target task with concatenated task-driven features  
The first partial task construction is a necessary preparation stage prior to distributed learning. In partial task construction stage, partial tasks are derived from the target task. The host party's partial task is specially called a merging task. The model structure of each partial task and merging task is derived, and similar feature spaces necessary for training are created. After this preparation stage is completed, each guest party's partial task and the host party's merging task are reorganized into a pre-training and fine-tuning task for the target task according to the transfer learning mechanism. In TT-VFDL-SIM, distributed learning for the target task occurs through three stages following the partial task construction stage, and these three stages are summarized in Figure 3.  
Figure 3: Distributed learning mechanism of TT-VFDL-SIM. The partial task model of each guest party and the merging task model of the host party have already been derived through partial task construction before the pre-training for the target task. Through Figure 3, we can see how the stages after the partial task construction in the task-driven transfer approach are applied to the design of the distributed learning mechanism of TT-VFDL-SIM. Each guest party trains its own partial task to pre-train the target task. The SIM Partial Training mechanism presents various methods of introducing similar feature spaces previously created into the training of each partial task.