https://github.com/HKUST-KnowComp/FKGE

Code for CIKM 2021 paper: Differentially Private Federated Knowledge Graphs Embedding (https://arxiv.org/abs/2105.07615)
https://github.com/HKUST-KnowComp/FKGE

differential-privacy federated-learning knowledge-graph

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Code for CIKM 2021 paper: Differentially Private Federated Knowledge Graphs Embedding (https://arxiv.org/abs/2105.07615)

• Host: GitHub
• URL: https://github.com/HKUST-KnowComp/FKGE
• Owner: HKUST-KnowComp
• Created: 2021-05-17T02:10:35.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-12-06T05:59:50.000Z (about 2 years ago)
• Last Synced: 2024-08-03T09:07:10.660Z (5 months ago)
• Topics: differential-privacy, federated-learning, knowledge-graph
• Language: Python
• Homepage:
• Size: 11.9 MB
• Stars: 29
• Watchers: 3
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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• StarryDivineSky - HKUST-KnowComp/FKGE

README

        

## Differentially Private Federated Knowledge Graphs Embedding:

### Data Release

The datasets we used for experiments have been partially uploaded.

To obtain all the KGs, you can find it in https://drive.google.com/file/d/1oD1Gv2RbpNzO8GWGq7SusbAmYih5r-6Q/view?usp=sharing.

Make sure to put KGs from the Google Drive into ```OpenKE/benchmarks```.

**Update**: The aligned files are updated and already put in the ```trainse_data/aligned``` folder.

### Package Dependencies

* numpy

* tensorflow 1.xx

* tensorflow_probability

### Baseline Embeddings

**You need to run the baseline experiments to obtain the KG embeddings through the following code**: 

```python Config.py baseline 300 100 1.0 -1```

The parameters denotes mode, epoches, dimension, gan_ratio and pred_id respectively. 

Note that if you want to try other embedding algorithms or some files like ```1-1.txt``` is missing, you need to run ```n_n.py``` from ```OpenKE/benchmarks``` for each KG in ```/OpenKE/benchmarks/KG_1```.

You can replace baseline with strategy_1 or strategy_2 to conduct the experiments with respect to FKGE. 

By running baseline embeddings, you will create a ```experiment/``` folder and the embeddings are inside ```experiment/0/``` if you sepcify ```pred_id=-1```.

### Federated Knowledge Graphs Embedding

**After obtaining KG's initital embeddings from running the baseline model (make sure there are embeddings in the ```experiment/0/``` folder), run**: 

```python Config.py strategy_1 300 100 1.0 0```

### DPFKGE

If you want to train FKGE with the *PATE* mechanism, in `Config.py`, replace 

```from FederalTransferLearning.hetro_AGCN_mul_dataset import GAN```

with

```from FederalTransferLearning.hetro_AGCN_mul_dataset_pate import GAN```

### Citation

* Paper: https://arxiv.org/abs/2105.07615

If you use this code in your work, please kindly cite it.

```

@inproceedings{Peng-2021-DPFKGE,

  title={Differentially Private Federated Knowledge Graphs Embedding},

  author={Hao Peng and

          Haoran Li and

          Yangqiu Song and

          Vincent W. Zheng and

          Jianxin Li},

  booktitle={CIKM 2021},

  year={2021},

  url={https://arxiv.org/abs/2105.07615}

}

```

### Miscellaneous

Please send any questions about the code and/or the algorithm to [email protected]