## 1 Introduction
We propose a new design approach of vertical federated deep learning suitable for the characteristics of the IoT domain, that is, the task-driven transfer approach. The task-driven transfer approach can overcome the limitations of the existing VFDL design approach in the IoT domain by selecting design strategies suitable for the characteristics of the IoT domain. These design strategies are implemented with four stages of a task-driven transfer approach considering the characteristics of multivariate IoT time-series data. First, considering the fact that each variable in multivariate IoT time-series data is dependent on each other, we fine-tune the multivariate target task based on task-driven features optimized for the univariate partial task. In addition, motivated by the importance of perturbation in IoT time-series data, unlike existing studies that remove perturbation, we prevent overfitting of task-driven features by injecting perturbation through similar feature spaces. In addition, we designed TT-VFDL-SIM as a VFDL method applying the proposed task-driven transfer approach and evaluated its performance with comparison methods that implemented the existing representative VFDL mechanism. TT-VFDL-SIM achieved an average of 0.00153 improved MSE and 7.98% improved accuracy in a communication-efficient manner compared to centralized training or existing VFDL methods. Focusing on the various characteristics of the IoT domain mentioned above, we devised this task-driven transfer approach and designed TT-VFDL-SIM based on it. Unlike existing VFDL methods designed by the training loop parallelization approach, the task-driven transfer approach is communication-efficient by leveraging the transfer learning mechanism. Also, unlike existing VFDL methods designed by the data-driven transfer approach, the task-driven transfer approach shows enhanced performance by leveraging task-driven features. Especially, the SIM Partial Training mechanism contributes to the performance improvement of TT-VFDL-SIM by preventing overfitting by introducing similar feature spaces in various ways to leverage perturbation. Through the proposed task-driven transfer approach and TT-VFDL-SIM designed based on it, a deep learning model defined on vertically partitioned multivariate IoT time-series data distributed among existing IoT services or data silos could be distributedly trained. The task-driven transfer approach can be applied to a deep learning model with separate multivariate inputs with a generalized structure from existing studies for multivariate IoT time-series data analysis. Additionally, the task-driven transfer approach can be applied to an architecture where each input and output is distributed to a separate existing IoT service, as in a real-world scenario. Therefore, TT-VFDL-SIM, implemented based on a task-driven transfer approach, can be used to construct high-quality healthcare services that combine data from each hospital and IoT companies. Or, it can be used to logically combine existing IoT services, such as constructing an integrated analysis service for new economic and business models that combine real economy data and virtual currency data. The contributions of our study are as follows:  
* The task-driven transfer approach contributed to research on transfer learning-based vertical federated deep learning for multivariate IoT time-series data by effectively utilizing the advantages of the transfer learning mechanism and characteristics of the IoT domain, unlike existing VFDL mechanisms. * TT-VFDL-SIM designed based on the task-driven transfer approach achieved better performance than centralized training and existing VFDL methods in a communication-efficient manner, and its applicability to cross-service distributed learning for multivariate IoT time-series data was validated. Therefore, new IoT services can be cost-effectively constructed through convergence between various domains.