Combined Use of Federated Learning and Image Encryption for Privacy-Preserving Image Classification with Vision Transformer

In addition, it can maintain the same accuracy as that of models normally trained with plain images. In recent years, privacy-preserving methods for deep learning have become an urgent problem. Accordingly, we propose the 2. Related Work combined use of federated learning (FL) and encrypted images for privacy-preserving image classification under the use of 2.1 Federated Learning (FL) the vision transformer (ViT). The proposed method allows us not only to train models over multiple participants without Federated Learning (FL) [4, 5] is the scheme proposed by directly sharing their raw data but to also protect the privacy Google, in which multiple data owners can collaborate on of test (query) images for the first time.