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:target: https://docs.ray.io/en/releases-2.8.0/
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:target: https://blades.readthedocs.io/en/latest/?badge=latest
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.. 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/raw/master/docs/source/images/dirichlet_partition.png
Sharding Partitioner
----------------------
.. image:: https://github.com/lishenghui/blades/raw/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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