DeceFL: A Principled Decentralized Federated Learning Framework

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Deep LearningDeceFL
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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