| Name | blades JSON |
| Version |
0.1.1234
JSON |
| download |
| home_page | |
| Summary | A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning |
| upload_time | 2023-11-22 14:17:48 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.9 |
| license | |
| keywords |
deep-learning
pytorch
federated-learning
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
.. .. raw:: html
.. <div style="text-align: center;">
.. container:: badges
.. image:: https://img.shields.io/badge/arXiv-2206.05359-red?logo=arxiv&style=flat-square&link=https%3A%2F%2Farxiv.org%2Fpdf%2F2206.05359.pdf
:alt: Static Badge
:target: https://arxiv.org/pdf/2206.05359.pdf
.. image:: https://img.shields.io/github/last-commit/lishenghui/blades/master?logo=Github
:alt: GitHub last commit (branch)
:target: https://github.com/lishenghui/blades
.. image:: https://img.shields.io/github/actions/workflow/status/lishenghui/blades/.github%2Fworkflows%2Funit-tests.yml?logo=Pytest&logoColor=hsl&label=Unit%20Testing
:alt: GitHub Workflow Status (with event)
.. image:: https://img.shields.io/badge/Pytorch-2.0-brightgreen?logo=pytorch&logoColor=red
:alt: Static Badge
:target: https://pytorch.org/get-started/pytorch-2.0/
.. image:: https://img.shields.io/badge/Ray-2.8-brightgreen?logo=ray&logoColor=blue
:alt: Static Badge
:target: https://docs.ray.io/en/releases-2.8.0/
.. image:: https://readthedocs.org/projects/blades/badge/?version=latest
:target: https://blades.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
.. image:: https://img.shields.io/github/license/lishenghui/blades?logo=apache&logoColor=red
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:target: https://github.com/lishenghui/blades/blob/master/LICENSE
.. .. raw:: html
.. <p align=center>
.. <img src="https://github.com/lishenghui/blades/raw/master/docs/source/images/arch.png" width="1000" alt="Blades Logo">
.. </p>
.. image:: https://github.com/lishenghui/blades/raw/master/docs/source/images/arch.png
Installation
==================================================
.. code-block:: bash
git clone https://github.com/lishenghui/blades
cd blades
pip install -v -e .
# "-v" means verbose, or more output
# "-e" means installing a project in editable mode,
# thus any local modifications made to the code will take effect without reinstallation.
.. code-block:: bash
cd blades/blades
python train.py file ./tuned_examples/fedsgd_cnn_fashion_mnist.yaml
**Blades** internally calls `ray.tune <https://docs.ray.io/en/latest/tune/tutorials/tune-output.html>`_; therefore, the experimental results are output to its default directory: ``~/ray_results``.
Experiment Results
==================================================
.. image:: https://github.com/lishenghui/blades/raw/master/docs/source/images/fashion_mnist.png
.. image:: https://github.com/lishenghui/blades/raw/master/docs/source/images/cifar10.png
Cluster Deployment
===================
To run **blades** on a cluster, you only need to deploy ``Ray cluster`` according to the `official guide <https://docs.ray.io/en/latest/cluster/user-guide.html>`_.
Built-in Implementations
==================================================
In detail, the following strategies are currently implemented:
Data Partitioners:
==================================================
Dirichlet Partitioner
----------------------
.. image:: https://github.com/lishenghui/blades/blob/master/docs/source/images/dirichlet_partition.png
Sharding Partitioner
----------------------
.. image:: https://github.com/lishenghui/blades/blob/master/docs/source/images/shard_partition.png
Citation
=========
Please cite our `paper <https://arxiv.org/abs/2206.05359>`_ (and the respective papers of the methods used) if you use this code in your own work:
::
@article{li2023blades,
title={Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning},
author= {Li, Shenghui and Ju, Li and Zhang, Tianru and Ngai, Edith and Voigt, Thiemo},
journal={arXiv preprint arXiv:2206.05359},
year={2023}
}
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