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.

翻译：我们提出FEDQ-Trust，一种创新的数据驱动信任预测方法，专为基于移动边缘的物联网环境设计。移动边缘环境的去中心化特性因数据分布差异带来挑战，影响现有分布式数据驱动信任预测模型的准确性和训练效率。FEDQ-Trust通过将联邦期望最大化算法与深度Q网络相结合，有效应对统计异质性挑战。联邦期望最大化算法对统计异质性的稳健处理显著提升了信任预测准确性。同时，深度Q网络简化了模型训练过程，在保持模型性能的前提下高效减少了训练客户端数量。我们利用两个真实物联网数据集，在模拟的基于移动边缘计算的物联网场景中开展了一系列实验。实验结果表明，与最先进模型相比，我们的模型在实现8%至14%的准确率显著提升的同时，收敛时间降低了97%至99%。