This is an implementation of the following papers:

[1] Salehkaleybar, S., Sharifnassab, A., & Golestani, S. J. (2021). One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them (Journal of Machine Learning Research).

[2] Sharifnassab, A., Salehkaleybar, S., & Golestani, S. J. (2019). Order Optimal One-Shot Distributed Learning (NeurIPS 2019).

If you find this code useful, please cite the following papers:

@article{sharifnassab2019order,
  title={Order Optimal One-Shot Distributed Learning},
  author={Sharifnassab, Arsalan and Salehkaleybar, Saber and Golestani, S Jamaloddin},
  journal={Advances in Neural Information Processing Systems},
  volume={32},
  pages={2168--2177},
  year={2019}
}

@article{salehkaleybar2019one,
  title={One-shot federated learning: theoretical limits and algorithms to achieve them},
  author={Salehkaleybar, Saber and Sharifnassab, Arsalan and Golestani, S Jamaloddin},
  journal={arXiv preprint arXiv:1905.04634},
  year={2019}
}

• Python 3.5+
• numpy
• scipy
• multiprocessing

In the current implementation, it is assumed that the number of samples per machine (n) is equal to one. In order to run instances of ridge regression problems for m=100000 number of machines, execute the following code:

$python MRE_C.py

The output is the average error on estimating the true parameters.