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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.

The continuous global growth of Internet of Things devices and deployments constantly produce an equally growing volume of streaming data. This volume puts high pressure on the cloud infrastructure that is expected to manage the storage and processing of such streams. Special purpose IoT data management platforms such as the IoT Framework which is the system under study in this paper, transform raw streaming data into actual information products thus providing value added services to interested users. The purpose of this paper is to evaluate and analyse the impact of the OpenID Connect protocol on the performance of the IoT Framework. The OpenID protocol was introduced in order to enable the IoT framework to be transformed into an IoT Information marketplace. The obtained results show that the introduction of an AA mechanism has a considerable impact on the performance of the IoT Framework. The reason is that the AA server is remote with respect to the IoT platform and therefore the network delay impacts the experienced end to end delay by the user. A possible means to alleviate this problem is to create a local AA server closer to the IoT Platform which either maintains local user AA credentials or federates with other public AA servers.