Security-Aware QoS Forecasting in Mobile Edge Computing based on Federated Learning

Abstract

This paper proposes a novel security-aware QoS (Quality of Service) forecasting approach - Edge QoS Per-PM (Edge QoS forecasting with Personalized training based on Public Models in mobile edge computing) by migrating the principle of integrating cooperative learning and independent learning from federated learning. Edge QoS Per-PM can make fast and accurate forecasting on the premise of ensuring enhanced security. We train private model based on public model for personalized forecasting. The private models are invisible to other users to ensure the absolute security. At regular intervals, a Long Short-Term Memory (LSTM) model is trained based on the latest private data to meet the realtime requirements of the dynamic edge environment and ensure the accuracy of prediction results. A series of experiments is conducted based on public network data sets. The results demonstrate that Edge QoS Per-PM can train appropriate models and achieve faster convergence and higher accuracy.