This is a simulator for distributed Machine Learning and Federated Learning on a single host. It implements common algorithms as well as our works.
This is a Python project. The third party dependencies are listed in **pyproject.toml **.
Use PIP to setup:
python3 -m pip install . --upgrade --force-reinstall --user To run the experiments of GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning, use this command
bash gtg_shapley_train.sh If you find our work useful, feel free to cite it:
@article{10.1145/3501811, author = {Liu, Zelei and Chen, Yuanyuan and Yu, Han and Liu, Yang and Cui, Lizhen}, title = {GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning}, year = {2022}, issue_date = {August 2022}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {13}, number = {4}, issn = {2157-6904}, url = {https://doi.org/10.1145/3501811}, doi = {10.1145/3501811}, journal = {ACM Trans. Intell. Syst. Technol.}, month = {may}, articleno = {60}, numpages = {21}, keywords = {Federated learning, contribution assessment, Shapley value} } To run the experiments of FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning, use this command
bash fed_obd_train.sh If you find our work useful, feel free to cite it:
@inproceedings{ijcai2023p394, title = {FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning}, author = {Chen, Yuanyuan and Chen, Zichen and Wu, Pengcheng and Yu, Han}, booktitle = {Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, {IJCAI-23}}, publisher = {International Joint Conferences on Artificial Intelligence Organization}, editor = {Edith Elkind}, pages = {3541--3549}, year = {2023}, month = {8}, note = {Main Track}, doi = {10.24963/ijcai.2023/394}, url = {https://doi.org/10.24963/ijcai.2023/394}, } The implementation has been move to other (GitHub repository)[https://github.com/cyyever/distributed_graph_learning_simulator]
If you find this work useful, feel free to cite it:
@article{li2024historical, title={Historical Embedding-Guided Efficient Large-Scale Federated Graph Learning}, author={Li, Anran and Chen, Yuanyuan and Zhang, Jian and Cheng, Mingfei and Huang, Yihao and Wu, Yueming and Luu, Anh Tuan and Yu, Han}, journal={Proceedings of the ACM on Management of Data}, volume={2}, number={3}, pages={1--24}, year={2024}, publisher={ACM New York, NY, USA} }