  The development of QoE models by means of Machine Learning (ML) is
challenging, amongst others due to small-size datasets, lack of diversity in
user profiles in the source domain, and too much diversity in the target
domains of QoE models. Furthermore, datasets can be hard to share between
research entities, as the machine learning models and the collected user data
from the user studies may be IPR- or GDPR-sensitive. This makes a decentralized
learning-based framework appealing for sharing and aggregating learned
knowledge in-between the local models that map the obtained metrics to the user
QoE, such as Mean Opinion Scores (MOS). In this paper, we present a transfer
learning-based ML model training approach, which allows decentralized local
models to share generic indicators on MOS to learn a generic base model, and
then customize the generic base model further using additional features that
are unique to those specific localized (and potentially sensitive) QoE nodes.
We show that the proposed approach is agnostic to specific ML algorithms,
stacked upon each other, as it does not necessitate the collaborating localized
nodes to run the same ML algorithm. Our reproducible results reveal the
advantages of stacking various generic and specific models with corresponding
weight factors. Moreover, we identify the optimal combination of algorithms and
weight factors for the corresponding localized QoE nodes.
