## 3 Task-Driven Transfer Approach
### Partial Task Construction
#### 3.1.1 DFS (Depth-First Search) Model Split Technique
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Design** & **Guest Party** & **Host Party** & **Communication** \\ \hline \multirow{2}{*}{\([\text{\emph{proposed}}]\)} & \multicolumn{2}{c}{**Model Structure**} & \multicolumn{2}{c}{**Model Structure**} & \\ TT-VFDL-SIM (RDD) * & \(f^{\lambda}\) & Partial Task & & \\ TT-VFDL-SIM (RDD) * & \(k=1,\ \dots,\ K\) & (FE + Split FM) & & Merging Task & One-shot \\  & for Regression & \(\sigma^{\lambda}_{\text{T}}\) & FE & (FM) & Communication \\ TT-VFDL-SIM (RR) * & \(k=1,\ \dots,\ K\) & \(k=1,\ \dots,\ K\) & & \\ TT-VFDL-SIM (RDD) * & \(\begin{array}{c}\text{\bf Pre-training}\\ \text{\bf for}\\ \text{\bf Target Task}\end{array}\) & \(\begin{array}{c}\text{\bf Sim}\\ \text{\bf for}\\ \text{\bf Supervised}\end{array}\) & \(\begin{array}{c}\text{\bf Fine-tuning}\\ \text{\bf for}\\ \text{\bf Target Task}\end{array}\) & \(\begin{array}{c}\text{\bf Fine-tuning}\\ \text{\bf for}\\ \text{\bf Target Task}\end{array}\) & \(\begin{array}{c}\text{\bf for}\\ \text{\bf Target Task}\end{array}\) & \(\begin{array}{c}\text{\bf Training}\\ \text{\bf(Supervised)}\end{array}\) \\ \hline \multirow{4}{*}{\([\text{\emph{Existing Mechanism}}]\)} & \multirow{4}{*}{Data-driven} & \(\sigma^{\lambda}_{\text{T}}\) & FE of Partial Task & & \\
**DT-VFDL-AE** & Transfer & \(d^{\lambda}_{\text{T}}\) & FE-based & (FM) & \(\begin{array}{c}\text{\bf One-shot}\\ \text{\bf Communication}\end{array}\) \\ \cline{2-5}  & & \(\sigma^{\lambda}_{\text{T}}\) & \(\text{\bf Decoder}\) & & \\ \cline{2-5}  & & **Pre-training** & Representation & **Fine-tuning** & Merging Task & \\  & & **for the** & Learning & **for the** & Training & \\  & & **Target Task** & (Unsupervised) & **Target Task** & (Supervised) & \\ \hline \multirow{4}{*}{\([\text{\emph{Existing Mechanism}}]\)} & \multirow{4}{*}{\([\text{\emph{Existing Mechanism}}]\)} & \multicolumn{2}{c}{**Forward Propagate (F.P.) \(\text{FE}\)**\(\bar{\sigma}^{\lambda}_{\text{T}}\)**} & \multirow{4}{*}{**Receive Feature \(F^{\lambda}_{\text{T}}\)**} & \multirow{4}{*}{**Receive Feature \(F^{\lambda}_{\text{T}}\)**} & \multirow{4}{*}{Communication**} \\  & & **Send Feature \(F^{\lambda}_{\text{T}}\)**} & \multirow{4}{*}{**Receive Feature \(F^{\lambda}_{\text{T}}\)**} & \multirow{4}{*}{**Receive Feature \(F^{\lambda}_{\text{T}}\)**} & \multirow{4}{*}{**Receive Feature \(F^{\lambda}_{\text{T}}\)**} & \multirow{4}{*}{**Receive Feature \(F^{\lambda}_{\text{T}}\)**} & \multirow{4}{*}{**Communication**} \\  & & **[P-VFDL-SLG]** & F.P. Partial Task \(\bar{\mu}\) & & Per \\
**P-VFDL-SLG** & \(\begin{array}{c}\text{\bf Pre}^{\lambda}_{\text{T}}\text{\bf-SLG}\\ \text{\bf Specoder}\end{array}\) & \(\begin{array}{c}\text{\bf Specoder}\\ \text{\bf Specoder}\end{array}\) & **Back Propagate FM \(f^{\lambda}\)** & & Every Step \\
**SNR** & & & & \\ \hline \hline \end{tabular}  
* The proposed TT-VFDL-SIM method with various method options.  
\end{table} 
Table 3: Overview of the proposed TT-VFDL-SIM and other comparison methods. The structures of the merging task model of TT-VFDL-SIM and DT-VFDL-AE are the same, but their inputs are different. The merging task model of TT-VFDL-SIM takes the task-driven features of each guest party as input. For this purpose, each guest party in TT-VFDL-SIM trains a partial task model consisting of the FE part and Split FM. On the other hand, the merging task model of DT-VFDL-AE takes the data-driven features of each guest party as input. For this purpose, each guest party trains an autoencoder for unsupervised representation learning, and the encoder structure of the autoencoder is the same as the FE part. Meanwhile, in P-VFDL-SL, each guest party trains the FE part distributedly through the first half of forward propagation and the second half of back propagation. The host party trains the FM part distributedly through the second half of forward propagation and the first half of back propagation.