## 2 Related Works
### Previous Work
#### 2.4.2 The Previous Method
With this approach, each party can obtain an effective source domain, that preserves the statistical properties of the target domain but is not identical to the target domain, from the use of only the raw data each party stores. Our approach is distinct from the existing studies that add augmented data to an insufficient training dataset [48,49] or use only augmented data instead of the original training dataset [39,40]. Therefore, the proposed self-transfer partial training mechanism is performed well since it generates an effective source domain for pre-training of the target domain that is shown in Figure 5. In Figure 5, decoded data, which is the source domain, is very similar to the raw data, which is the target domain. The decoded data have almost the same data distribution as the raw data but are slightly different in each value. Especially in the transfer learning for sensor data, the noise oriented from the hardware characteristics of the sensor should also be considered [50]. That is, the decoded data are a source domain that reflect meaningful variants like noise and also preserves the statistical properties of the target domain. Also, we could empirically verify these assumptions in the experiments. * _Then, why is decoded data (reproduced data) used in the pre-training of each partial task, rather than the raw data?_
* The first reason is to improve performance. * The second reason is to improve applicability in real-world system operation. The first reason could be found in the previous experiments of [7]; In the situation that only one feature space should be selected, the overall performances were higher when introducing a feature space produced with reproduced data rather than raw data. Additionally, we experimentally verified that the overall performance could be improved by pre-training leveraging reproduced data [7]. The second reason could also be easily inferred. If the reproduced data are used in the fine-tuning of each partial task, the decoder trained to generate reproduced data should be maintained to transfer trained univariate task-driven features to the host party. In real-world scenarios, maintaining a well-trained decoder could be overhead from the perspective of operation. Therefore, the self-transfer partial training mechanism pre-trains each partial task in a feature space generated from the reproduced data to find an approximate optimal solution that does not overfit the univariate partial task. After that, by fine-tuning the partial task in the feature space generated from raw data, a single mechanism is finally completed to train task-driven features that help improve final performance without overfitting the

Figure 5: Real patterns of raw data and its decoded data with data distributions of each raw data and decoded data. univariate partial task. We call this kind of our training mechanism'self-transfer' since it is a transfer learning mechanism between two feature spaces produced from each raw data and its reproduced data, respectively, without using the other party's data. * According to our research, yes. As far as we could find, there was no existing research for the transfer learning mechanism exactly the same as this approach. There was an existing study [51] using the term'self-transfer learning', but [51] was a study related to CNN-based weakly supervised learning on image data. In other words, ref. [51] is a study to solve a problem different from this paper, and the'self-transfer learning' in [51] is also a training mechanism that is different from the self-transfer partial training mechanism we propose. ### Algorithms of TT-VFDL-ST

We designed two algorithms of TT-VFDL-ST, for regression and classification since the elements of two feature spaces used in the self-transfer partial training mechanism are different depending on the type of task. The algorithm for regression tasks is described in Algorithm 3 and for classification tasks is described in Algorithm 4.