DeceFL: A Principled Decentralized Federated Learning Framework

Last update: May 31, 2022

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Deep Learning DeceFL

Overview

DeceFL: A Principled Decentralized Federated Learning Framework

This repository comprises codes that reproduce experiments in Ye, et al (2021), which has been submitted to Nature Machine Intelligence.

Project Organization

Experiments:

Comparative studies of DeceFL and FedAvg, SL on dataset A2, provided in DatasetA2/.
Time-vary experiments for DeceFL using dataset A2
- Time-varying graphs with edge changes, provided in DatasetA2/
- Time-varying graphs with node changes, provided in NodeVarying/
Comparative study of DeceFL and FedAvg, SL on CWRU dataset, provided in CWRU/.
An consensus example is generated by scripts in ConsensusExample/.

Go to each folder for README.md for every experiment.

Dependencies

Hardware: GPU that supports Pytorch

OS: Linux, Windows 10

Python packages:

torch == 1.9.0
numpy == 1.21.0
sklearn == 0.24.2
pandas == 1.3.1
tqdm == 4.46.0
matplotlib == 3.4.2

More to be filled ...

Reference

[1] Ye Yuan, et al. DeceFL: A Principled Decentralized Federated Learning Framework. Submitted to Nature Machine Intelligence, 2021.

Owner

Huazhong Artificial Intelligence Lab (HAIL)

Huazhong Artificial Intelligence Lab (HAIL)

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