## 4 Tt-Vfd-Sim
### TT-VFDL-SIM for Classification
The merging task is fine-tuned based on task-driven features trained through univariate partial tasks. TT-VFDL-SIM can achieve improved performance over centralized training and existing VFDL methods in a communication-efficient manner by taking advantage, which is suitable for the multivariate IoT time-series domain, of both task-driven features and the transfer learning mechanism. In particular, TT-VFDL-SIM introduces similar feature spaces to partial task training in various ways based on the SIM Partial Training mechanism. Through the introduction of these similar feature spaces, it is possible to obtain basic security for not exposing raw data in a completely distributed architecture and prevent overfitting for univariate partial tasks. In the experiment, the impact of introducing similar feature spaces and task-driven features on improving the performance of TT-VFDL-SIM was analyzed, and through this, the effectiveness of the proposed task-driven transfer approach was verified. In addition, the detailed operational principles and suitability of each stage of the task-driven transfer approach, such as the SIM Partial Training mechanism and merging task training, were also experimentally verified. In the experiment, the MSE of TT-VFDL-SIM applied to the regression target tasks decreased by up to 0.00181, 0.00188, and 0.01143 compared to CNTR, P-VFDL-SL, and DT-VFDL-AE, respectively. In addition, the accuracy of TT-VFDL-SIM applied to the classification target task increased by up to 4.82%, 4.82%, and 33.73% compared to CNTR, P-VFDL-SL, and DT-VFDL-AE, respectively. TT-VFDL-SIM has been able to improve the performance of the target task by specifying various methods of introducing similar feature spaces through various method options and feature options of the SIM Partial Training mechanism. However, there is a limitation in that it does not present a single mechanism that can generalize various similar feature spaces' introduction techniques implemented with various method options and feature options. Up to this point, we experimentally analyzed the impact of various method options and feature options in SIM Partial Training on the performance of the target task and concluded that it is advantageous to introduce a similar feature space generated based on decoded data. However, this has not been generalized into the design of fixed similar feature spaces' introduction techniques that can be consistently applied to the various datasets and structures of the target task model. In future works, we plan to study generalized methods for various method options and feature options of the SIM Partial Training mechanism. Methodology, software, and writing, S.O. ; review and editing, M.L. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2021R1F1A1062559). Not applicable. No new data were created or analyzed in this study. The program codes to perform the experiments using five IoT time-series datasets are available online at [[http://dwlab.ewha.ac.kr/mlee/codes/tt-vfdl-sim/source.7z](http://dwlab.ewha.ac.kr/mlee/codes/tt-vfdl-sim/source.7z)]([http://dwlab.ewha.ac.kr/mlee/codes/tt-vfdl-sim/source.7z](http://dwlab.ewha.ac.kr/mlee/codes/tt-vfdl-sim/source.7z)) (accessed on 1 March 2024). The programs are written in Python. Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. ## References

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