A Multi-Modal Tensor Ring Decomposition for Communication-Efficient and Trustworthy Federated Learning for ITS in COVID-19 Scenario

Traffic and the movement of people are inextricably associated with the potential spread of COVID-19. In Intelligent Transportation System (ITS), Deep Learning (DL) traffic detection approaches driven by transportation big data have significant application values in monitoring, counting and classifying traffic vehicle information during the COVID-19 epidemic blockade, while DL COVID-19 medical diagnostic technology is also very important. However, due to concerns about data privacy and security, traditional data-centralized DL techniques that require uploading training data from multiple cameras or hospitals are no longer suitable. Federated Learning (FL) as a novel collaborative privacy-preserving DL paradigm could address this issue well. Nevertheless, in FL, most existing works train learning models with full-precision weights and communicate them over multiple iterations, which may incur massive additional communication costs and disclose the privacy implied in the trained local models. To tackle these issues, we first propose a novel multi-modal tensor ring decomposition TR-TSVD that not only achieves efficient data reduction but also keeps the correlations among multi-modes. Afterward, applying TR-TSVD to the training process of a convolutional neural network under the FL framework to achieve the goal of reducing communication overhead while ensuring model performance. Additionally, since the weight parameters are transmitted with the TR-TSVD format, attackers cannot infer the data privacy without knowing the specific restoration method. Besides, the additively homomorphic encryption is leveraged to further preserve model security. Extensive experimental results on MNIST, BIT-Vehicle and COVID-CT datasets show that the proposed approach could achieve a better performance.