FEDQ-Trust: Efficient Data-Driven Trust Prediction for Mobile Edge-Based IoT Systems

TL;DR

FEDQ-Trust is a novel data-driven trust prediction method for mobile edge IoT systems that combines federated learning techniques with deep reinforcement learning to improve accuracy and training efficiency in heterogeneous environments.

Contribution

It introduces FEDQ-Trust, integrating Federated Expectation-Maximization with Deep Q Networks to address heterogeneity and reduce training time in trust prediction for mobile edge IoT.

Findings

Achieved 97-99% reduction in convergence time.

Improved trust prediction accuracy by 8-14%.

Validated on real-world IoT datasets.

Abstract

We introduce FEDQ-Trust, an innovative data-driven trust prediction approach designed for mobile edge-based Internet of Things (IoT) environments. The decentralized nature of mobile edge environments introduces challenges due to variations in data distribution, impacting the accuracy and training efficiency of existing distributed data-driven trust prediction models. FEDQ-Trust effectively tackles the statistical heterogeneity challenges by integrating Federated Expectation-Maximization with Deep Q Networks. Federated Expectation-Maximization's robust handling of statistical heterogeneity significantly enhances trust prediction accuracy. Meanwhile, Deep Q Networks streamlines the model training process, efficiently reducing the number of training clients while maintaining model performance. We conducted a suite of experiments within simulated MEC-based IoT settings by leveraging two real-world IoT datasets. The experimental results demonstrate that our model achieved a significant convergence time reduction of 97% to 99% while ensuring a notable improvement of 8% to 14% in accuracy compared to state-of-the-art models.