# Combined Use of Federated Learning and Image Encryption for Privacy-Preserving Image Classification with Vision Transformer Source: [Combined Use of Federated Learning and Image Encryption for Privacy-Preserving Image Classification with Vision Transformer]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:21:27.168Z Authors: Teru Nagamori, Hitoshi Kiya Published: 2023-01-23T03:41:02.000Z ## Abstract 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. ## 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]()