A Deep Federated Learning-Based User Credit Evaluation Model Under Financial Internet of Things Scenarios

Abstract

With the rapid development of Internet of Things (IoT) technology and the digital transformation of the financial industry, the financial IoT is becoming an important trend in the future financial field. In the era of financial IoT, a large amount of data information is recorded by sensors and devices, which poses new challenges and opportunities for user credit evaluation. In this context, conventional deep learning-based user credit evaluation methods often cannot meet privacy needs. The paper combines the privacy security ability of federated learning with deep learning, and proposes a deep federated learning-based user credit evaluation model under financial IoT scenarios. First, a particle swarm optimization-based backpropagation neural network algorithm is formulated to extract user credit evaluation features, in order to obtain user behavior patterns and credit features. Then, the XGBoost algorithm is employed to output credit evaluation results. As for the training process, the thought of federated learning is integrated to distribute the model to individual participants. In such mode, the model can be updated and trained without exposing user data to the central cloud. The experiments are conducted on real-world data to assess the proposal’s performance. The results show that the proposed credit evaluation framework can achieve better accuracy, under the guarantee of user privacy.