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

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