## 4 Tt-Vfd-Sim
### TT-VFDL-SIM for Classification
In other words, for each dataset, three target task models were constructed by varying only the FM part structure for the same FE part structure. The three target task models for each dataset can be distinguished according to the FM part structure, and the FM part structures are SLFM, MLFM, and MFCMLFM as shown in Figure 1. The FE part structure for each dataset was constructed as summarized in the FE Structure column of Table 5.

Through preliminary experiments based on centralized training, the type of FE part structure optimized for each dataset was determined, and the values of various hyperparameters related to the specific structure of each target task model were determined. In other words, the type of FE part structure for each dataset, detailed layer-by-layer design of each target task model, and various hyperparameter values were determined to ensure the best performance in centralized training. #### 5.1.4 Target System Architecture and Comparison Methods

The target system architecture of TT-VFDL-SIM is a completely distributed architecture in which each input and output are distributed. As shown in Figure 2, TT-VFDL-SIM and other comparison methods were implemented assuming a completely distributed architecture consisting of three guest parties and one host party. There are a total of four other comparison methods for evaluating the performance of TT-VFDL-SIM, as shown in Figure 2. CNTR is a centralized training mechanism. P-VFDL-SL, P-VFDL-SLG, and DT-VFDL-AE are three representative mechanisms of the existing VFDL methods introduced in Section 2.4. Table 6 summarizes the characteristics of the comparison methods used in the experiment and the proposed method. This study was conducted from the perspective of the VFDL mechanism, and the goal is to design a new VFDL mechanism suitable for the characteristics of the IoT domain, unlike the existing VFDL mechanism. Therefore, existing studies that can represent existing VFDL methods from a VFDL mechanism perspective were selected as comparison methods. From a VFDL mechanism perspective, not only the existing [2; 3; 8; 9; 10; 11; 12; 13; 14; 15] but also the recent VFDL methods [4; 5; 6; 7] belong to one of P-VFDL-SL, P-VFDL-SLG, and DT-VFDL-AE in Table 6.

P-VFDL-SL applies Split Learning [2] to our target system architecture. The submodel of each guest party is the FE part for each input of the target task model, and the submodel of the host party is the FM part of the target task model. P-VFDL-SL parallelizes the forward propagation and back propagation that constitute the training loop through each guest party and host party. The mechanism of existing VFDL methods [2; 11; 12; 13; 14; 15] of the training loop parallelization approach is generally based on Split Learning [2]. Therefore, P-VFDL-SL is a representative mechanism of existing VFDL methods based on the training loop parallelization approach. P-VFDL-SLG implemented [16], which improved performance by adding a local gradient update mechanism to Split Learning, to suit the experimental scenario. DT-VFDL-AE is a representative mechanism of existing VFDL methods of the data-driven transfer approach [3; 8; 9; 10]. Each guest party trains data-driven features through unsupervised representation learning and transfers them to the host party. Since autoencoders are generally used for the unsupervised representation learning of each guest party, each guest party in DT-VFDL-AE also trains an autoencoder. This autoencoder is based on an encoder with the same structure as the FE part structure of the target task model for each guest party's input. #### 5.1.5 Experimental Setup

In this paper, experiments were conducted on each of the five datasets in Table 5 based on three target task models composed of different types of FM parts to the same FE part. As summarized in Section 2.1, these target task models are a generalization of the multivariate deep learning model structure of existing studies on multivariate IoT time-series analysis.