README.md

VR-ProxSkip

This is the official code repository for NeurIPS 2022 paper: Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning

Requirement

We run each experiment with 64G CPU and Nvidia A100-80G GPU. The estimated running time for each experiments is 30min ~ 6hs (mostly relying on the number of maximum communication rounds).

We are required to install the following packages

python=3.8
optmethods
ray
psutil
numpy
matplotlib
copy
argparse
scikit-learn
urllib
other common package

After that, we need to modify one of the intalled optmethods functions in python package, Main change is to modify this function with the following code (should be in anaconda3/envs/$PROJECT_NAME$/lib/python3.8/site-packages/optmethods):

def compute_loss_of_iterates(self, return_loss_vals=False):
    for seed, loss_vals in self.loss_vals_all.items():
        if loss_vals is None:
            self.loss_vals_all[seed] = np.asarray([self.loss.value(x) for x in self.xs_all[seed]])
        else:
            warnings.warn("""Loss values for seed {} have already been computed. 
                Set .loss_vals_all[{}] = [] to recompute.""".format(seed, seed))
    self.loss_is_computed = True
    if return_loss_vals:
        return self.loss_vals_all[seed]

Convergence Analysis

We provide the code to compare VR-ProxSkip with baselines in `main_0002.py'. One optional script to run it is:

python main_0002.py --batch_size 16 --dataset 'a9a' --it_local 20 --choose_p 'kappa' --it_max 10001

Total Cost Ratio

The example script to obtain the total cost ratio:

python main_0004.py --batch_size 16 --cerr 1e-8 --regul 5e-4 --it_max 8000 --dataset 'a9a'

ProxSkip-QLSVRG

The example script to run ProxSkip-QLSVRG:

python main_0007.py --batch_size 16 --dataset 'a9a' --it_local 20 --choose_p 'kappa' --it_max 15001 --k 11

Citation

@article{vrproxskip2022,
  title={Variance reduced proxskip: Algorithm, theory and application to federated learning},
  author={Malinovsky, Grigory and Yi, Kai and Richt{\'a}rik, Peter},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={15176--15189},
  year={2022}
}

Acknowledgement

This repo contains VR-ProxSkip related implementations building on top of opt_methods.