Towards Efficient, Credible and Privacy-Preserving Service QoS Prediction in Unreliable Mobile Edge Environments
Yilei Zhang, Peiyun Zhang, Yonglong Luo, Liya Ji · 2020
With the widespread adoption of the fifth-generation (5G) cellular network and Mobile Edge Computing (MEC), numerous Internet of Things (IoT) applications are emerging in many critical areas. IoT applications are typically running on mobile devices to provide real-time interaction with users by connecting with smart IoT devices and remote cloud services. In order to ensure the performance of IoT applications, Quality of Service (QoS) is commonly used as a key metric for the selection and adaptation of high-quality services at runtime. Collaborative QoS prediction methods have been proposed in the literature to predict personalized QoS values, enabling QoS-based selection and adaptation. However, privacy issues in collaborative QoS prediction discourage users from collaborating by sharing data in practice. Furthermore, there are untrusted users in the unreliable MEC environment, which makes the prediction encounter serious reliability issues. As a result, privacy and reliability issues have become key challenges to make QoS prediction approaches feasible. In this paper, we proposed a credible and privacypreserving QoS prediction approach by leveraging federated learning techniques and developing reputation mechanisms to address this critical challenge. We evaluate the method on a large- scale real QoS dataset and the experimental results demonstrate the effectiveness and efficiency of the method.