Fully-Decentralized Federated Learning for QoE Estimation

Abstract

In the past, Quality of Service (QoS) was taken into account to evaluate the performance of multimedia services (e.g., video streaming, file transfer, etc.). However, it cannot reflect the user's perception, which is considered a crucial consideration by these services nowadays. Therefore, the emergence of Quality of Experience (QoE) is a potential solution. QoE can be measured via many parameters provided by Internet Service Providers (ISP), Application Service Providers (ASP), or end-users. However, privacy concerns hinder data sharing between the parties involved. To address these limitations, this paper proposes a QoE estimation mechanism that leverages Federated Learning. This mechanism aims to guarantee data privacy when no party needs to disclose their data to others. Moreover, the proposed mechanism incorporates the concept of a Decentralized Autonomous Organization (DAO) to mitigate the risk of a single point of failure in the centralized architecture of Federated Learning. It enables all participants to evaluate and select the model efficiently. The experimental results illustrate that the proposal surpasses the centralized solutions and guarantees data privacy.