# Enhanced Security with Encrypted Vision Transformer in Federated Learning Source: [Enhanced Security with Encrypted Vision Transformer in Federated Learning]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:36:15.259Z Authors: Rei Aso, Sayaka Shiota, Hitoshi Kiya Published: 2023-08-01T04:05:55.000Z ## Abstract Federated learning is a learning method for training models over multiple participants without directly sharing their raw data, and it has been expected to be a privacy protection method for training data. In contrast, attack methods have been studied to restore learning data from model information shared with clients, so enhanced security against attacks has become an urgent problem. Accordingly, in this article, we propose a novel framework of federated learning on the bases of the embedded structure of the vision transformer by using the model information encrypted with a random sequence. In image classification experiments, we verify the effectiveness of the proposed method on the CIFAR-10 dataset in terms of classification accuracy and robustness against attacks. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()