Combined Use of Federated Learning and Image Encryption for Privacy-Preserving Image Classification with Vision Transformer
This addresses privacy concerns for users in image classification by protecting both training and test data, though it is incremental as it builds on existing FL and encryption techniques.
The paper tackles privacy-preserving image classification by combining federated learning and image encryption with Vision Transformers, achieving no performance degradation on CIFAR-10 and CIFAR-100 datasets.
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.