## 3 Task-Driven Transfer Approach
### Merging Task Training  
Task-driven features can be extracted based on the FE part of the partial task model trained by the SIM Partial Training mechanism in each guest party. As shown in Figure 3, the task-driven features of each guest party are transmitted to the host party through one-shot communication. The merging task of the host party is trained to output the raw data of the host party based on the concatenated feature constructed by combining task-driven features from each guest party. Combination information between each guest party that was not considered when training the univariate partial task is trained through merging task training.  
In general, horizontal federated learning implements federated learning by aggregating the weights trained by each client on the server. This is because each client generally trains a submodel with the same structure in common. However, in representative existing VFDL mechanisms, there is no submodel of the same structure that is commonly trained by each party. Therefore, weight aggregation is generally not performed in existing VFDL mechanisms.  
In the proposed TT-VFDL mechanism based on the task-driven transfer approach, submodels with the same structure that are commonly trained between each guest party do not always exist. Therefore, basically, weight aggregation is not performed in merging task training. Instead, the merging task fine-tunes the target task based on task-driven features by training a merging task model with randomly initialized weights. This is because in the task-driven transfer approach, the target task is distributedly trained based on transfer learning between partial tasks derived from the target task and the merging task. However, unlike existing VFDL mechanisms, the TT-VFDL mechanism can sometimes define submodels with the same structure that are commonly trained between each guest party depending on the structure of the target task model. We are conducting future research on weight aggregation techniques for this case.  
Meanwhile, merging task training is fine-tuning for the target task. Generally, in transfer learning, the learning rate of fine-tuning is set to a lower value than the learning rate in pre-training. Then what should be the learning rate for merging task training? Instead of uniformly setting the learning rate for merging task training to a small, fixed value, we derived an appropriate learning rate for each dataset and target task model through experimentation.  
The reason why the learning rate of merging task training is not fixed to a specific value is because the feature space dimensions in pre-training and fine-tuning are different. In general transfer learning, pre-training and fine-tuning are performed based on feature spaces of the same dimension. On the other hand, in our merging task training, we fine-tune the target task in a multivariate feature space with more expanded dimensions than the univariate feature space of each partial task. Since the impact of feature space dimension expansion on the learning rate of the fine-tuning task is not yet explained, this paper derived the appropriate learning rate of merging task training for each dataset and target task model through experiments.