  Machine Learning based Quality of Experience (QoE) models potentially suffer
from over-fitting due to limitations including low data volume, and limited
participant profiles. This prevents models from becoming generic. Consequently,
these trained models may under-perform when tested outside the experimented
population. One reason for the limited datasets, which we refer in this paper
as small QoE data lakes, is due to the fact that often these datasets
potentially contain user sensitive information and are only collected
throughout expensive user studies with special user consent. Thus, sharing of
datasets amongst researchers is often not allowed. In recent years, privacy
preserving machine learning models have become important and so have techniques
that enable model training without sharing datasets but instead relying on
secure communication protocols. Following this trend, in this paper, we present
Round-Robin based Collaborative Machine Learning model training, where the
model is trained in a sequential manner amongst the collaborated partner nodes.
We benchmark this work using our customized Federated Learning mechanism as
well as conventional Centralized and Isolated Learning methods.
