Installation

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.

cd blades/blades
python train.py file ./tuned_examples/fedsgd_cnn_fashion_mnist.yaml

Blades internally calls ray.tune; therefore, the experimental results are output to its default directory: ~/ray_results.

Experiment Results

Cluster Deployment

To run blades on a cluster, you only need to deploy Ray cluster according to the official guide.

Built-in Implementations

In detail, the following strategies are currently implemented:

Attacks

General Attacks

Strategy	Description	Sourse
Noise	Put random noise to the updates.	Sourse
Labelflipping	Fang et al. Local Model Poisoning Attacks to Byzantine-Robust Federated Learning, USENIX Security' 20	Sourse
Signflipping	Li et al. RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets, AAAI' 19	Sourse
ALIE	Baruch et al. A little is enough: Circumventing defenses for distributed learning NeurIPS' 19	Sourse
IPM	Xie et al. Fall of empires: Breaking byzantine- tolerant sgd by inner product manipulation, UAI' 20	Sourse

Adaptive Attacks

Strategy	Description	Sourse
DistanceMaximization	Shejwalkar et al. Manipulating the byzantine: Optimizing model poisoning attacks and defenses for federated learning, NDSS' 21	Sourse

Defenses

Robust Aggregation

Strategy	Descriptions	Source
MultiKrum	Blanchard et al. Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent, NIPS'17	Source
GeoMed	Chen et al. Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent, POMACS'18	Source
Median	Yin et al. Byzantine-robust distributed learning: Towards optimal statistical rates, ICML'18	Source
TrimmedMean	Yin et al. Byzantine-robust distributed learning: Towards optimal statistical rates, ICML'18	Source
CenteredClipping	Karimireddy et al. Learning from History for Byzantine Robust Optimization, ICML'21	Source
Clustering	Sattler et al. On the byzantine robustness of clustered federated learning, ICASSP'20	Source
ClippedClustering	Li et al. An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning, IEEE TBD'23	Source
DnC	Shejwalkar et al. Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning, NDSS'21	Source
SignGuard	Xu et al. SignGuard: Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filtering, ICDCS'22	Source

Data Partitioners:

Dirichlet Partitioner

Sharding Partitioner

Citation

Please cite our paper (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}
}