Federated Learning-driven Trust Prediction for Mobile Edge Computing-based IoT Systems

We propose a federated learning-based data-driven trust prediction method to meet the demand of high-accuracy IoT service trustworthiness prediction in Mobile Edge Computing (MEC) with low convergence time. Our research focuses on the mixture distribution and heterogeneity features of IoT trust information in distributed MEC environments and formulates the task of distributed IoT trust prediction on top of MEC network topologies as a federated optimization problem. We then employ Federated Expectation-Maximization to mitigate the federated optimization problem by taking into account the data mixture distribution and heterogeneity. We conduct a series of experiments upon simulated MEC-based IoT environments crafted on top of a real-world IoT dataset. The experimental results show that our proposed methods can achieve better balance between prediction accuracy and model training efficiency than a state-of-the-art data-driven MEC-based IoT service trust prediction method and a Federated Averaging-based method.