Skip to content

Latest commit

 

History

History
39 lines (33 loc) · 1.42 KB

README.md

File metadata and controls

39 lines (33 loc) · 1.42 KB
Raw

Federated Deep AUC Maximization pdf

This is the official implementation of the paper "Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity" published on ICML2021.

Requirements

Tensorflow >= 2.0.0
PyTorch >= 1.3.0

How to run

python -m torch.distributed.launch --nproc_per_node=4 --nnodes=1 --node_rank=0 --master_addr='YOUR IP' --master_port=8888 \
            main_codasca_cifar.py --T0=4000 --imratio=0.1 --gamma=500 --lr=0.1 --I=8 --local_batchsize=32 --total_iter=20000

Citation

If you find this repo helpful, please cite the following paper:

@InProceedings{DBLP:conf/icml/YuanGXYY21,
  title = 	 {Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity},
  author =       {Yuan, Zhuoning and Guo, Zhishuai and Xu, Yi and Ying, Yiming and Yang, Tianbao},
  booktitle = 	 {Proceedings of the 38th International Conference on Machine Learning},
  pages = 	 {12219--12229},
  year = 	 {2021},
  editor = 	 {Meila, Marina and Zhang, Tong},
  volume = 	 {139},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {18--24 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v139/yuan21a/yuan21a.pdf},
  url = 	 {https://proceedings.mlr.press/v139/yuan21a.html},
}