Differentially Private Federated Tensor Completion for Cloud–Edge Collaborative AIoT Data Prediction
Zecan Yang, Botao Xiong, Kai Chen, Laurence T. Yang, Xianjun Deng, Chenlu Zhu, Yuanyuan He
Abstract
Artificial Intelligence of Things (AIoT) is an emerging paradigm that integrates artificial intelligence (AI) and Internet of Things (IoT) technologies to provide intelligent IoT solutions. The AIoT system acquires data in real time through IoT sensors, performs intelligent data analysis tasks anywhere in the terminal–edge–cloud continuum, and provides accurate decision-making services based on data predictions. Cloud–edge collaboration can reduce security risks for AIoT data prediction by sharing data features instead of raw data. However, sensitive user data may still be inferred by attackers through model parameter analysis, causing irreparable harm and serious consequences. Therefore, data prediction based on cloud–edge collaboration while maintaining privacy constraints remains a significant challenge. In this article, a differentially private federated tensor completion method is proposed for cloud–edge collaborative AIoT data prediction. This method embeds differential privacy (DP) mechanisms with cloud–edge collaboration. Each edge is capable of processing and analyzing data, and collaborative learning with other edges by sharing privacy-preserving model parameters. For model security, objective perturbation is applied to ensure that the tensor completion method satisfies DP. To achieve higher accuracy, parallel tensor decomposition is introduced to avoid the update conflicts problem of federated tensor completion. Through theoretical analysis, our method can provide data protection for tensor completion with high-security promise. The experiments are performed on both synthetic and real-world data sets to demonstrate the superior performance of our method in preserving data privacy.