vio1etus/FLPoison

FLPoison: Benchmarking Poisoning Attacks and Defenses in Federated Learning

Python

18 commits

updated Apr 15, 2026

See the code

README

Welcome to FLPoison

Last Commit

Check the Wiki to get started with the project.

Features

PyTorch's implementation of poisoning attacks and defenses in federated learning.

Category	Details
FL Algorithms	FedAvg, FedSGD, FedOpt(see fl/algorithms)
Data Distribution	Balanced IID, Class-imbalanced IID, Quantity-imbalanced Dirichlet Non-IID, (Quantity-Balanced\|-Imbalanced) Pathological Non-IID (see data_utils.py)
Datasets	MNIST, FashionMNIST, EMNIST, CIFAR10, CINIC10, CIFAR100, CHMNIST, TinyImageNet (see dataset_config.yaml)
Models	Logistic Regression, SimpleCNN, LeNet5, ResNet-series, VGG-series

Supported datasets and models pairs see datamodel.pdf

Federated Learning Algorithms

Name	Source File	Paper
FedSGD	fedsgd.py	Communication-Efficient Learning of Deep Networks from Decentralized Data - AISTATS '17
FedAvg	fedavg.py	Communication-Efficient Learning of Deep Networks from Decentralized Data - AISTATS '17
FedOpt	fedopt.py	Adaptive Federated Optimization - arxiv '20, ICLR '21

Attacks and Defenses

Applicable algorithms include base algorithms used by original paper, as well as others not explicitly mentioned but applicable based on the described principles. [ ] indicates necessary modifications for compatibility, also implemented within this framework. To sum up, we implemented and adapted the attacks and defenses to be compatible with three commonly-used FL algorithms, FedSGD, FedOpt, FedAvg.

Data Poisoning Attacks (DPAs)

Data poisoning attacks here, mainly targeted attacks, refer to attacks aimed at embedding backdoors or bias into the model, thus misleading it to produce the attacker's intended prediction

Name	Source File	Paper	Base Algorithm	Applicable Algorithms
Neurotoxin	neurotoxin.py	Neurotoxin: Durable Backdoors in Federated Learning - ICML '22	FedOpt	FedOpt, [FedSGD, FedAvg]
Edge-case Backdoor	edgecase.py	Attack of the Tails: Yes, You Really Can Backdoor Federated Learning - NeurIPS '20	FedOpt	FedSGD, FedOpt, [FedAvg]
Model Replacement Attack (Scaling Attack)	modelreplacement.py	How to Backdoor Federated Learning - AISTATS '20	FedOpt	FedOpt, [FedSGD, FedAvg]
Alternating Minimization	altermin.py	Analyzing Federated Learning Through an Adversarial Lens - ICML '19	FedOpt	FedSGD, FedOpt, [FedAvg]
DBA	dba.py	DBA: Distributed Backdoor Attacks Against Federated Learning - ICLR '19	FedOpt	FedSGD, FedOpt, [FedAvg]
BadNets	badnets.py	BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain - NIPS-WS '17	Centralized ML	[FedSGD, FedOpt, FedAvg]
Label Flipping Attack	labelflipping.py	Poisoning Attacks against Support Vector Machines - ICML'12	Centralized ML	[FedSGD, FedOpt, FedAvg]

Defenses Against DPAs

Name	Source File	Paper	Base Algorithm	Applicable Algorithms
FLAME	flame.py	FLAME: Taming Backdoors in Federated Learning - USENIX Security '22	FedOpt	FedOpt,[FedSGD, FedAvg]
DeepSight	deepsight.py	DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection - NDSS '22	FedOpt	FedOpt, [FedSGD, FedAvg]
CRFL	crfl.py	CRFL: Certifiably Robust Federated Learning against Backdoor Attacks - ICML '21	FedOpt	FedOpt, [FedSGD, FedAvg]
NormClipping	normclipping.py	Can You Really Backdoor Federated Learning - NeurIPS '20	FedOpt	FedOpt, [FedSGD, FedAvg]
FoolsGold	foolsgold.py	The Limitations of Federated Learning in Sybil Settings - RAID '20	FedSGD	FedSGD, [FedOpt, FedAvg]
Auror	auror.py	Auror: Defending against poisoning attacks in collaborative deep learning systems - ACSAC '16	FedSGD	FedSGD, [FedOpt, FedAvg]

Model Poisoning Attacks (MPAs)

Model poisoning attacks here, main untargeted attacks, refer to the attacks aimed at preventing convergence of the model, thus affecting the model's performance.

Name	Source File	Paper	Base Algorithm	Applicable Algorithms
HIDRA Attack	hidra.py	Attacking Byzantine Robust Aggregation in High Dimensions - IEEE S&P '24	FedSGD	FedSGD, [FedOpt, FedAvg]
Mimic Attack	mimic.py	Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing - ICLR '22	FedSGD	FedSGD, [FedOpt, FedAvg]
Min-Max attack	min.py	Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning - NDSS '21	FedSGD	FedSGD, [FedOpt, FedAvg]
Min-Sum attack	min.py	Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning - NDSS '21	FedSGD	FedSGD, [FedOpt, FedAvg]
Fang attack (Adaptive attack)	fangattack.py	Local Model Poisoning Attacks to Byzantine-Robust Federated Learning - USENIX Security '20	FedAvg	[FedSGD, FedOpt], FedAvg
IPM attack	ipm.py	Fall of empires: Breaking Byzantine-tolerant SGD by inner product manipulation - UAI '20	FedSGD	FedSGD, [FedOpt, FedAvg]
ALIE attack	alie.py	A Little Is Enough: Circumventing Defenses For Distributed Learning - NeurIPS '19	FedSGD	FedSGD, [FedOpt, FedAvg]
Sign flipping attack	signflipping.py	Asynchronous Byzantine machine learning (the case of SGD) - ICML '18	FedSGD	FedSGD, [FedOpt, FedAvg]
Gaussian (noise) attack	gaussian.py	Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent - NeurIPS '17	FedSGD	FedSGD, [FedOpt, FedAvg]

Defenses Against MPAs

Name	Source File	Paper	Base Algorithm	Applicable Algorithms
LASA	lasa.py	Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregation - WACV '25	FedOpt	FedSGD, [FedOpt, FedAvg]
FedSign	fedsign.py	FedSIGN: A sign-based federated learning framework with privacy and robustness guarantees - Computers & Security 2023	FedSGD	[FedSGD], FedOpt, FedAvg]
FLDetector	fldetector.py	FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients - KDD '22	FedSGD	FedOpt, [FedOpt, FedAvg]
SignGuard	signguard.py	Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filtering - ICDCS '22	FedSGD	FedSGD, [FedOpt, FedAvg]
Bucketing	bucketing.py	Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing - ICLR '22	FedSGD	FedSGD, [FedOpt, FedAvg]
DnC	dnc.py	Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning - NDSS '21	FedSGD	FedSGD, [FedOpt, FedAvg]
CenteredClipping	centeredclipping	Learning from History for Byzantine Robust Optimization - ICML '21	FedSGD	FedSGD, [FedOpt, FedAvg]
FLTrust	fltrust.py	FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping - ArXiv'20, NDSS '21	FedOpt	FedOpt, [FedSGD, FedAvg]
RFA (Geometric Median)	rfa.py	Robust Aggregation for Federated Learning - ArXiv'19, TSP '22	FedAvg	[FedSGD, FedOpt], FedAvg
Bulyan	bulyan.py	The hidden vulnerability of distributed learning in Byzantium - ICML'18	FedSGD	FedSGD, [FedOpt,FedAvg]
Coordinate-wise Median	median.py	Byzantine-robust distributed learning: Towards optimal statistical rates - ICML'18	FedSGD	FedSGD, [FedOpt, FedAvg]
Trimmed Mean	trimmedmean.py	Byzantine-robust distributed learning: Towards optimal statistical rates - ICML'18	FedSGD	FedSGD, [FedOpt, FedAvg]
Multi-Krum	multikrum.py	Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent - NeurIPS '17	FedSGD	FedSGD, [FedOpt, FedAvg]
Krum	krum.py	Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent - NeurIPS '17	FedSGD	FedSGD, [FedOpt, FedAvg]
SimpleClustering	simpleclustering.py	Simple majority-based clustering	FedSGD, FedAvg, FedOpt	FedSGD, FedAvg, FedOpt

Contributing

Bug reports, feature suggestions, and code contributions are welcome. Please open an issue or submit a pull request if you encounter any problems or have suggestions.

Citation

If you are using FLPoison for your work, please cite our paper with:

@misc{sokflpoison,
      title={SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning},
      author={Heyi Zhang and Yule Liu and Xinlei He and Jun Wu and Tianshuo Cong and Xinyi Huang},
      year={2025},
      eprint={2502.03801},
      archivePrefix={arXiv},
      primaryClass={cs.CR},
      url={https://arxiv.org/abs/2502.03801},
}

adversarial-attacks

benchmarking

byzantine-robust

federated-learning

poisoning-defenses

pytorch

robust-machine-learning

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