Federated Learning Averaging Algorithm

This is a simple implementation of Federated Learning (FL) . The bare FL model (without DP) is the reproduction of the paper Communication-Efficient Learning of Deep Networks from Decentralized Data.

Requirements

• torch 1.9.19
• numpy 1.20.3

Files

main.py: start this project

center.py: server of the federated learning

client.py: client of the federated learning

datatset.py: data of mnist

algorithm.py: fedavg algorithm

models.py: all kind of models

Usag

1. Run python main.py

Reference

[1] McMahan, Brendan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proc. Artificial Intelligence and Statistics (AISTATS), 2017.