metadata
license: mit
language:
- en
base_model:
- openai/clip-vit-large-patch14
tags:
- privacy
- transformers
Privacy-Preserving Split Learning via Patch Shuffling over Transformers
Paper: https://ieeexplore.ieee.org/abstract/document/10027647
API of Patch Shuffling
PatchShuffle
function: utilsenc.PatchShuffle(x)->y
x: input feature; y: outputfeature
BatchShuffle
function: utilsenc.BatchPatchPartialShuffle(x,k1)->y
x: input feature; k: proportions of patches not to be shuffle; y: outputfeature
SpectralShuffle
The function is the same as PatchShuffle or BatchShuffle, but first turn models into spectral domain. Please see the example as reference.
Citation Bibtex
@INPROCEEDINGS{patchshuffling,
author={Yao, Dixi and Xiang, Liyao and Xu, Hengyuan and Ye, Hangyu and Chen, Yingqi},
booktitle={2022 IEEE International Conference on Data Mining (ICDM)},
title={Privacy-Preserving Split Learning via Patch Shuffling over Transformers},
year={2022},
pages={638-647},
doi={10.1109/ICDM54844.2022.00074}
}
D. Yao, L. Xiang, H. Xu, H. Ye and Y. Chen, "Privacy-Preserving Split Learning via Patch Shuffling over Transformers," 2022 IEEE International Conference on Data Mining (ICDM), Orlando, FL, USA, 2022, pp. 638-647, doi: 10.1109/ICDM54844.2022.00074.