# QoE Modeling on Split Features with Distributed Deep Learning
## 6 Results
### Optimizing \(vFL\) Training: Data Volume and Training Time Perspectives
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Neuron Count** \\ **at the Cut-Layer** \\ \end{tabular} } & \multirow{2}{*}{**F1-Score**} & \multicolumn{2}{c}{**Network Footprint [Bytes]**} & \multicolumn{2}{c}{**Training Time [s]**} \\ \cline{3-6}  & & **[PERSON] ON** & **[PERSON] OFF** & **[PERSON] ON** & **[PERSON] OFF** \\ \hline
2 & 0.75 \(\pm\) 0.05 & 5413 \(\pm\) 3 & 6348 \(\pm\) 8 & 1472 \(\pm\) 372 & 1378 \(\pm\) 150 \\ \hline
4 & 0.85 \(\pm\) 0.03 & 8102 \(\pm\) 7 & 10,725 \(\pm\) 17 & 1161 \(\pm\) 98 & 1281 \(\pm\) 122 \\ \hline
**8** & **0.86 \(\pm\) 0.01** & **13,522 \(\pm\) 11** & **19,087 \(\pm\) 30** & **1313 \(\pm\) 24** & **1106 \(\pm\) 119** \\ \hline
16 & 0.87 \(\pm\) 0.01 & 23,899 \(\pm\) 22 & 34,165 \(\pm\) 48 & 1806 \(\pm\) 158 & 1624 \(\pm\) 32 \\ \hline
32 & 0.87 \(\pm\) 0.01 & 44,602 \(\pm\) 45 & 66,322 \(\pm\) 118 & 2885 \(\pm\) 166 & 1545 \(\pm\) 114 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average test set F1-score, network footprint, and total training time together with 95% confidence intervals for different neuron counts at the cut-layers of both workers. Final remarks on comparison of \(DEL\) and \(vFL\) are given together with the conventional _Isolated_ and _Fully Centralized_ scenarios in Table 4.
* _Data share:_ Only in _Fully Centralized_ training methodology, all the datasets that are collected in different other entities are being transferred to a centralized computation node to train a ML model. * _Model share:_ Since the data is not shared in \(DEL\) and \(vFL\), the models have to be shared. In \(DEL\), all layers of the NN model at the source domain are transferred to the target domain, while in \(vFL\), only the computed output (so-called smashed wisdom) at the intermediate cut-layers are transferred to the master node. In return the worker nodes receive the subparts of gradients computed at the cut-layer of the master node. * _Sample dependency:_ An _Isolated_ model can be trained with data instances of choice that are available to the local computation node, hence there is no sample dependency on data instances available at other nodes. In _Fully Centralized_ scenario, the data instances are collected from different nodes hence the overall model is dependent on the data instances obtained from the workers. Although in \(DEL\), data instances at the source domain are used in training, as the pre-trained source model is transferred to the master target domain, there is no dependency on the data instances at the source node during inference phase. In contrast in \(vFL\), since the data instances need to be synchronized in time or in space, the output of the final model highly depends on the input data instance from all workers. * _Feature dependency:_ As the local computation nodes can train on any available features at the local nodes, there is no feature dependency in _Isolated_ learning. In a _Fully Centralized_ setting, all local nodes need to provide exactly the same feature space to a central  
Figure 16: Average test set F1-score, network footprint, and total training time for different batch size settings over multiple rounds, when cut-layer neuron count was set to 8 with Elias encoding. computation node, hence we consider that there is a feature dependency. In \(DEL\), there is a requirement that at least some subset of the features need to be common both on other source and target domain so that a knowledge transfer from source domain to target domain on generic features can be transferred. In \(vFL\), all features located at the distributed nodes can be different, hence there is no feature dependency. * _Jointly trainable: Fully Centralized_ setting allows collaborative training with samples received from different worker nodes. In \(DEL\), joint training is not possible as the knowledge transfer is unidirectional and also it is performed after pre-training phase.