# QoE Modeling on Split Features with Distributed Deep Learning
## 6 Results
### Optimizing \(vFL\) Training: Data Volume and Training Time Perspectives
The most outstanding advantage of \(vFL\) is its ability to allow joint training and inference, hence all workers can potentially benefit and perform continuous training. * _Aggregation:_ In \(DEL\), the aggregation of outputs are performed by means of weighted averaging, while in \(vFL\) the intermediate output of the workers and the computed gradients are aggregated via concatenation and splitting procedure. It is again important to emphasize that in \(DEL\), aggregation occurs only in inference phase, while in \(vFL\), it is performed both in training and inference phases and in every round of training. * _Model agnostic:_ As long as the full dataset is available at a computation node such as in the cases of _Isolated_, _Fully Centralized_, and \(DEL\), there is no model dependency, hence any suitable ML model can be selected and trained. For instance, in \(DEL\), the model that is transferred from the source domain can be of any algorithm, since it is not the model that is being aggregated but instead the output of the models. The \(vFL\) is based on Neural Network algorithm where the training occurs with a Stochastic Gradient Descent (SGD), and it is an important requirement for workers to train on the same algorithm (although not necessarily the exactly the same NN model structure) and update the local neural weights. * _Worker contribution adjustment:_ In _Isolated_ setting there is only one worker, which is the local individual node. In _Fully Centralized_ setting, the data instances from all workers are fed into the ML model in batches, hence the ML model adjusts its weights depending on all data instances. Therefore the contributions from every worker get mixed. In \(DEL\), there is a manual weight adjustment process during the inference on the validation set depending on the magnitude of contribution of source domain model on the final output at the target domain. \(vFL\) adjusts the weights during the training process over the rounds, hence the adjustment of contribution of workers is seamless and automatic. * _Network Footprint at the master and worker:_ The network footprint is given in the form of mathematical formulation for both worker (source in \(DEL\) and master (target in \(DEL\)) nodes. The expected network footprint is presented for all scenarios. \(W\) is the worker; \(r\) is the round; \(s\) is the data instance; \(f\) is the feature; \(l\) is the NN layer; and \(n\) are the neurons at layer, \(l\); \(c\) is the neuron at the NN cut-layer of worker, \(w\). As there is no data or model shared in between training entities in Isolated learning, the network footprint is 0. In Fully Centralized scenario, all data instances and features from all workers have to be transferred to the central computation node. In \(DEL\), the data is not shared; however, the pre-trained model, which consists of multiple NN layers with potentially many neurons at every layer, at the source domain are transferred to the master node for only once. In \(vFL\), only the output of the cut-layer for all data instances and rounds is shared. In return, the worker nodes receive the gradients calculated for the data instances, hence the traffic is bidirectional so that the total sum is multiplied by 2. * _Accuracy:_ In _Isolated_ setting, the dataset size is small, and the model training is limited to local observations which might prevent the model to achieve accuracy values that a _Fully Centralized_ model (trained on with richer and large size dataset) can achieve. We consider _Fully Centralized_ as a model that can reach upper bound accuracy levels, and moreover it is possible to reach on-par accuracy values without sharing datasets using both \(DEL\) and \(vFL\).