# QoE Modeling on Split Features with Distributed Deep Learning
### Distributed Learning in QoE
_DEL_ is a technique that utilizes that property by averaging the output of the pre-trained models at the source domain(s) with the target domain model output to maximize the accuracy at the target domain. The datasets in the source domains need to have at least a subset of the feature set of the target domain. It is a suitable technical solution, but requires tuning the weights between the source, and the target model by monitoring metrics that quantify data diversity such as Kullback-Leibler (KL) divergence [15]. We mainly propose and evaluate \(vFL\), which is based on a split neural network architecture, where each split partition of one big neural network (NN) model can reside on different physical computation nodes. This allows each worker to train a NN model jointly even on completely different feature sets on each participating node. In addition, since all worker nodes including the master node collaborate to train one single machine learning model, the information exchange in between them is bidirectional. The requirement is a consensus and synchronization in between the participating nodes about the task they are solving. In the case of multi-sensor distributed local datasets, e.g., IoT datasets, the observations that are collected on different distributed local nodes need to be from the same time interval. Similarly, in the case of QoE user studies, any observations that are collected via different measurement modules from the same users in different labs would need to follow the same sequential way of assessment. It could be for instance also applicable in the cases of a very large-scale user study where many participants experience the exact same video (or accomplish the same task), while every research entity considers only subparts of the observations of the subjects due to limitations of sensor availability, or assessment tools in the user labs. For instance, one research entity is collecting data from one particular user with sensor set A, while another entity uses sensor set B. The main differences between \(DEL\) and \(vFL\) techniques are illustrated in Figure 3. The local feature sets are denoted by S0 and S1 for the groups 0 and 1, respectively. The trained models in every node are denoted by \(M_{i}\), where \(i\) denotes the group id where \(M\) is trained. The target variables that the models aim to estimate are denoted by \(y_{i}\). In \(DEL\), the pre-trained model at the source domain (G1) with features S1 is transferred to the target domain (G0). Two separate inference processes are then executed at G0; one via the received \(M_{1}\) model with generic features S1, and one with \(M_{0}\) model with both S0 and S1. Finally, the two outputs from the two models, \(y^{\prime}_{i}\) are averaged to obtain the final model output. In \(vFL\), there is no transfer of model from source to target domain, instead they are trained jointly. The intermediate representation output at the cut-layer of the neural network models in \(vFL\) in both groups are given with matrices, \(X^{\prime}_{i}\). The latter are then transferred to the master node and are used as input to the master neural network model. Finally, the joint model output \(y^{\prime}\) is obtained. In \(DEL\) and \(vFL\), there is no requirement for the computation nodes to have the exact same set of attributes. In \(DEL\), the source domain should have at least a subset of the features of the target domain (e.g., shareable non-sensitive feature subset); and in \(vFL\), the attributes can be totally orthogonal as they can be completely decoupled. Detailed descriptions of \(DEL\) and \(vFL\) are given in Section 5. Overall, we consider \(vFL\) to be more flexible than \(DEL\) and consider \(DEL\) as rather a baseline. Figure 1: Comparison of conventional Centralized and Isolated Training scenarios.