| --- |
| 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. |
|
|
