In recent years, privacy-preserving methods for deep learning have become an urgent problem. Accordingly, we propose the combined use of federated learning (FL) and encrypted images for privacy-preserving image classification under the use of the vision transformer (ViT). The proposed method allows us not only to train models over multiple participants without directly sharing their raw data but to also protect the privacy of test (query) images for the first time. In addition, it can also maintain the same accuracy as normally trained models. In an experiment, the proposed method was demonstrated to well work without any performance degradation on the CIFAR-10 and CIFAR-100 datasets.

翻译：近年来，深度学习的隐私保护方法已成为一个紧迫问题。为此，我们提出将联邦学习与加密图像联合用于视觉Transformer下的隐私保护图像分类。该方法不仅允许在多个参与者间无需直接共享原始数据即可训练模型，还首次实现了测试（查询）图像的隐私保护。此外，该方法能够保持与常规训练模型相同的准确率。实验表明，所提方法在CIFAR-10和CIFAR-100数据集上运行良好且无性能下降。