Secure sparse gradient aggregation with various computer-vision techniques for cross-border document authentication and other security applications

Artificial-intelligence (AI) applications need a large amount of data to train a reliable model. For document authentication - which is relevant for border management, immigration or visa applications - this data is very sensitive. To develop document authentication technology for authorities from multiple countries, it is essential to train AI models on the distributed datasets provided by each authority. Federated learning (FL) enables the training on datasets of multiple organizations while preserving the privacy by sharing only the model updates (gradients) and not the local data. This helps avoiding the cross-border sharing of personal data. However, there are two main concerns related to FL: the communication costs and the possible leakage of personal data through the model updates. The solution can be found in secure sparse gradient aggregation (SSGA). In this method, we use top-k compression to speed up the communication. Additionally, a residual memory is implemented to improve performance. The aggregation is made more secure by adding pairwise noise to the gradients. In this paper, we show that SSGA can be implemented for various computer-vision tasks, such as image classification, object detection, semantic segmentation, and person re-identification, which are relevant for document authentication and other security applications.