FEDQ-Trust: Efficient Data-Driven Trust Prediction for Mobile Edge-Based IoT Systems
arxiv(2024)
摘要
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
a notable improvement of 8
models.
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