## 4 Tt-Vfd-Sim
### TT-VFDL-SIM for Classification
However, in the regression target task, setting the learning rate of merging task training to be larger or smaller than the learning rate of partial task training tended to help improve performance. Meanwhile, in classification target tasks, setting the learning rate of merging task training to be equal to or greater than the learning rate of partial task training tended to help improve performance. The performance of merging task training and the average performance of partial task training showed a significant linear correlation for all experimental units. In other words, in TT-VFDL-SIM, generally, the higher the average performance of partial task training, the higher the performance of merging task training. Meanwhile, in the regression target task, the average performance of partial task training using method option DD showed a higher linear correlation with the performance of merging task training than the method option RD. Additionally, in the classification target task, the average performance of partial task training using method option DR showed a higher linear correlation with the performance of merging task training than method option RR. In other words, task-driven features trained from similar feature spaces generated based on decoded data, rather than raw data, are not overfitted to univariate partial tasks and can be considered more suitable for merging tasks. The task-driven transfer approach improves the performance of TT-VFDL-SIM by leveraging transfer learning mechanisms in various ways. First, the target task is trained based on a transfer learning mechanism that fine-tunes the merging task based on task-driven features pre-trained from partial tasks. TT-VFDL-SIM is communication-efficient due to this transfer learning mechanism. Also, TT-VFDL-SIM can achieve excellent performance, unlike existing VFDL methods of the data-driven transfer approach, by leveraging task-driven features rather than data-driven features. Meanwhile, VFDL methods of transfer learning-based approaches, including TT-VFDL-SIM, only need to align features to train the merging task. In other words, TT-VFDL-SIM and DT-VFDL-AE do not need to consider data alignment between each guest party during partial task training. On the other hand, existing VFDL methods of the training loop parallelization approach, such as P-VFDL-SL, must align the raw data of each guest party [44; 45; 46]. TT-VFDL-SIM leverages a transfer learning mechanism to enable distributed learning with improved performance while being independent of data alignment issues between guest parties. Finally, feature options for SIM Partial Training's method options DD or DR extracted task-driven features by applying the raw data to a trained partial task model based on similar feature spaces generated from decoded data. Because the raw data were not used to train the partial task model, this is a transfer learning mechanism that applies the knowledge trained on the decoded data to the raw data. In this way, partial task training using transfer learning helped improve the performance of TT-VFDL-SIM compared to partial task training without it. As a result, the task-driven transfer approach achieved improved performance in a communication-efficient manner compared to CNTR and existing VFDL methods by utilizing various advantages of transfer learning. ## 6 Conclusions

In this paper, we propose the TT-VFDL-SIM and the task-driven approach to design the TT-VFDL-SIM. The proposed TT-VFDL-SIM is a vertical federated deep learning method that can achieve improved performance in a more communication-efficient manner than centralized training or existing VFDL methods for multivariate IoT time-series analysis. The task-driven transfer approach derives univariate partial tasks and a merging task from a multivariate target task and applies a transfer learning mechanism for the target task based on them. Univariate partial tasks and a merging task are reorganized as pre-training and fine-tuning tasks for the target task, respectively.