This code repository is for "Federated Accelerated Stochastic Gradient Descent" authored by Honglin Yuan (Stanford) and Tengyu Ma (Stanford), published in NeurIPS 2020 (best paper in FL-ICML'20 workshop).

proceeding | video (3 min) | poster (pdf)

bibtex:

@inproceedings{NEURIPS2020_39d0a890,
 author = {Yuan, Honglin and Ma, Tengyu},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
 pages = {5332--5344},
 publisher = {Curran Associates, Inc.},
 title = {Federated Accelerated Stochastic Gradient Descent},
 url = {https://proceedings.neurips.cc/paper/2020/file/39d0a8908fbe6c18039ea8227f827023-Paper.pdf},
 volume = {33},
 year = {2020}
}

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Dependencies:

• python 3.7 with the following packages: numpy, matplotlib, scipy, pandas, sklearn
• Datasets a9a and epsilon can be downloaded from https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/.
  • please place datasets to libsvm_datasets directory. Unzip epsilon.

Scripts:

• logistic.py: main functions
• analysis_utils.py: auxiliary functions for analysis

To reproduce the results in paper: (please be aware that the following scripts can take long time to run.)

• Figure 1, 2: a9a with l2 reg = 1e-3: run python a9a_1e-03.py;
• Figure 3, 4: a9a with l2 reg = 1e-2: run python a9a_1e-02.py;
• Figure 5, 6: epsilon with l2 reg = 1e-4: run python epsilon_1e-04.py;
• Figure 7: a9a with l2 reg = 1e-4: run python a9a_1e-04.py. These commands will generate results in out directory.

Alternatively, you can plot the figures directly based on our results in Jupyter notebook browse_figures.ipynb.