PyTorch code that accompanies the paper Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation. Our work contains Knowledge Graph Reconstruction Attack and the federated version of the following Knowledge Graph algorithms: TransE, RotatE, ComplEx, DistMult, NoGE and KB-GAT.
There is a data preprocessing file dataset.ipynb which allows split triples randomly.
We does not provide original data that can be downloaed online, but we provide a federated version of DDB14 in folder --Fed_data.
There is script exp.sh running all federated experiments with non-gnn models, while the codes of Fed-NoGE are in folder --NoGE.
Some examples of reconstruction attack are shown in rec_attack_fedr_fb15k_TransE.ipynb as well as in the folder --FedE/Rec_Attack/...
Please cite our paper if you find this code useful for your research. For any clarification, comments, or suggestions please create an issue or contact [email protected]
@article{fedr,
title={Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation},
author={Zhang, Kai and Wang, Yu and Wang, Hongyi and Huang, Lifu and Yang, Carl and Chen, Xun and Sun, Lichao},
journal={Findings of EMNLP},
year={2022},
}