# Privacy Preserving QoE Modeling using Collaborative Learning

## TL;DR

This paper introduces a privacy-preserving collaborative learning approach for QoE modeling that enables multiple parties to train shared models without exposing sensitive data, addressing data scarcity and privacy concerns.

## Contribution

It proposes a novel Round-Robin based collaborative training method combined with federated learning, benchmarking against centralized and isolated approaches.

## Key findings

- The proposed method maintains data privacy during training.
- Collaborative learning improves model generalization.
- Benchmark results show competitive performance with privacy guarantees.

## Abstract

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.