Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning [NeurIPS'2021]

Official code repository for our accepted work "Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning" in the Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) 2021:

Xinyi Xu, Lingjuan Lyu\, Xingjun Ma, Chenglin Miao, Chuan Sheng Foo, Bryan Kian Hsiang Low

Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning Paper

Set up environment using conda

Tested OS platform: Ubuntu 20.04 with Nvidia driver Version: 470.86 CUDA Version: 11.4

conda env create -f environment.yml

Running the main.py

Running on MNIST dataset with 5 agents and uniform data split (i.e., I.I.D). Automatically uses GPU if available.

python main.py -D mnist -N 5 -split uni

Results directory

The results are saved in csv formats in a RESULTS directory (created if not exist) by default.

Citing

If you have found our work to be useful in your research, please consider citing it with the following bibtex:

@inproceedings{Xu2021,
   author = {Xu, Xinyi and Lyu, Lingjuan and Ma, Xingjun and Miao, Chenglin and Foo, Chuan Sheng and Low, Bryan Kian Hsiang},
   booktitle = {Advances in Neural Information Processing Systems},
   editor = {M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan},
   pages = {16104--16117},
   publisher = {Curran Associates, Inc.},
   title = {Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning},
   volume = {34},
   year = {2021}
}