## 1 Introduction  
Attempts to build expanded services by combining existing IoT services while reusing the infrastructure and storage of existing IoT services [1] can effectively reduce the costs required for service integration techniques [2, 3, 4]. By introducing the integrated IoT service concept in Figure 1, expanded services can be constructed cost-effectively by logically combining existing IoT services while preserving their independent data architecture. To maintain privacy, the raw data of existing services that consist of the integrated IoT service cannot be shared with each other. Therefore, in order to construct an integrated IoT service, a multivariate deep learning model is defined and distributedly trained, with inputs consisting of raw data from existing services. The multivariate deep learning model for an integrated IoT service is defined based on vertically partitioned data, so it is distributedly trained between services using vertical federated learning methods [5, 6]. In our previous work [7], we could classify existing methods of vertical federated learning applied to deep learning into two groups from the perspective of the distributed learning mechanism. Even the latest vertical federated learning methods fall into one of these two groups in terms of distributed learning mechanisms. Existing methods [8, 9, 10, 11] are efficient from the perspective of communication. However, especially in the IoT domain, sometimes their performances are lower than that of the centralized method. Meanwhile, the performances of existing methods [12, 13, 14, 15, 16, 17] are similar to centralized methods even in the IoT domain. However, their training requires a very large size of data. Meanwhile, we proposed a new approach to design a mechanism of vertical federated learning on a deep learning model in our previous work [7]. Our design approach considers characteristics of IoT domains, unlike the existing two groups. Also, our approach combines only the advantages of the existing two groups in terms of a distributed learning mechanism. First, our approach utilizes task-driven features for improved performance since task-driven features trained by considering the aimed task are more suitable in the IoT domain rather than features obtained by unsupervised representation learning. Second, our approach leverages transfer learning for a distributed learning mechanism to achieve communication efficiency. In our previous work [7], we designed a vertical federated learning method using our design approach above. It achieved the best performance than centralized training or the two groups of existing mechanisms, with a small communication data. However, it has a limitation in that it is difficult to apply to real-world-integrated IoT service construction scenarios. One of the core strategies of our approach is introducing two feature spaces similar to each other for the training of features considering the aimed task [7]. Our previous method consists of various options to introduce similar feature spaces [7]. These options make it not organized into a consistent mechanism. Also, the best option from the perspective of performance may depend on the data or task characteristics. It makes our previous method difficult to apply to real-world scenarios. To overcome this limitation of our previous method [7], we first propose a self-transfer partial training mechanism in this paper. This is a single mechanism to effectively introduce similar feature spaces regardless of data or task characteristics. Also, we design TT-VFDL-ST (Task-driven Transferred Vertical Federated Deep Learning using Self-Transfer partial training), a new vertical federated learning method transferring task-driven features and  
Figure 1: An integrated IoT service scenario consisting of a logical combination of existing IoT services: Hospital A, Hospital B, and IT Company C maintain separate data collection infrastructure and data architecture for their existing IoT services \(S_{1}\), \(S_{2}\), and \(S_{3}\).