Machine Learning

An unofficial PyTorch implementation of a federated learning algorithm

Federated Averaging (FedAvg) in PyTorch

An unofficial implementation of FederatedAveraging (or FedAvg) algorithm proposed in the paper Communication-Efficient Learning of Deep Networks from Decentralized Data in PyTorch. (implemented in Python 3.9.2.)

Implementation points

  • Exactly implement the models (‘2NN’ and ‘CNN’ mentioned in the paper) to have the same number of parameters written in the paper.
    • 2NN: TwoNN class in models.py; 199,210 parameters
    • CNN: CNN class in models.py; 1,663,370 parameters
  • Exactly implement the non-IID data split.
    • Each client has at least two digits in case of using MNIST dataset.
  • Implement multiprocessing of client update and client evaluation.
  • Support TensorBoard for log tracking.

Requirements

Configurations

Run

Results

MNIST