Skip to content

Latest commit

 

History

History
98 lines (73 loc) · 5.53 KB

README.md

File metadata and controls

98 lines (73 loc) · 5.53 KB
Raw

LoDen

Cite LoDen

If you use LoDen for research, please cite the following paper:

@article{Ma2023LoDen,
title={LoDen: Making Every Client in Federated Learning a Defender Against the Poisoning Membership Inference Attacks},
author={Ma, Mengyao and Zhang, Yanjun and Arachchige, Pathum Chamikara Mahawaga and Zhang, Leo Yu and Baruwal Chhetri, Mohan and Bai, Guangdong},
booktitle={18th ACM ASIA Conference on Computer and Communications Security {ASIACCS 2023'}},
year={2023},
publisher={ACM}
}

Code structure

To run the experiments, please run setting_optimized.py, replace setting with the LoDen knowledge settings to experiment with. Here, there are three key runable files in the repository, including

  • loden_blackbox_defence.py # Blackbox LoDen defence experiment
  • loden_whitebox_defence.py # Whitebox based LoDen defence experiment
  • loden_MIA.py # Basic MIA experiment

Other files in the repository

  • constants.py Experiment constants, contains the default hyperparameter set for each experiment

  • data_reader.py It is used for read dataset, split dataset into training set and test set for each participant

  • aggregator.py Server-side robust aggregators, including

    • Fang [1]
    • Median [2]
    • Trimmed-Mean [2]
    • FLTrust [3]
    • Multi-Krum [4]

  • models.py The models including target and attack model are wrapped in this file, and also including

    • Basic target and attack models
    • MIA algorithm

Instructions for running the experiments

1. Set the experiment parameters

The experiment parameters are defined in constants.py. To execute the experiments, please set the parameters in constants.py file.

2. Run the experiment

To run the experiment, please run loden_setting_defence.py, replace setting with the LoDen knowledge settings to experiment with. You can use command line to run the experiment, e.g. in a LINUX environment, to execute the Black-box LoDen experiment, please input the following command under the source code path

python loden_blackbox_defence.py

To execute the whitebox LoDen experiment, please input the following command under the source code path

python loden_whitebox_defence.py

To execute the MIA baseline experiment, please input the following command under the source code path

python loden_MIA.py

3. Save the experiment results

After the experiment is finished, the experiment results will be saved in the directory defined in constants.py . The experiment results include the Local nodes log, Attacker log, LoDen defence log and the FL training log.

Understanding the output

The result of the experiment will be saved in the directory defined in constants.py (default output directory), including

  • Local nodes log file (E.g. the log for local node 0, 2023_04_21_22Location30MedianTrainEpoch100predicted_vector_round0100optimized_model_single_honest_side_defence_0.csv): This file contains the training samples log, including:

    • class prediction: the class prediction of the sample
    • class label: the class label of the sample
    • the confidence vector of groundtruth class: the probability of the groundtruth class prediction
    • sample index: the index of the sample

  • Attacker log file (E.g.2023_04_21_22Location30MedianTrainEpoch100predicted_vector_round0100optimized_model_single_defence.csv): This file contains the victim samples log, including:

    • class prediction: the class prediction of the sample
    • class label: the class label of the sample
    • the confidence vector of groundtruth class: the probability of the groundtruth class prediction
    • sample index: the index of the sample

  • LoDen defence log file (E.g.2023_04_21_22Location30MedianTrainEpoch100predicted_vector_round0100optimized_model_single_honest_side_defence_removed.csv): This file contains the local node samples removed by LoDen log, including:

    • sample removed round: the round of the FL training when the sample is removed
    • sample class belongs: the class label of the sample
    • sample index: the index of the sample

  • FL training log file (E.g.2023_04_21_22Location30MedianTrainEpoch100AttackEpoch100honest_node_round0optimized_model_single_ascent_factor15_honest_side_defence.txt): This file contains the FL training log, including:

    • round: the round of the FL training
    • test loss: the test loss of the global model for current round
    • test accuracy: the test accuracy of the global model for current round
    • train accuracy: the train accuracy of the global model for current round
    • time: the current time of the FL training

Requirements

Recommended to run with conda virtual environment

  • Python 3.10.5
  • PyTorch 1.13.0
  • numpy 1.22.4
  • pandas 1.4.2

Reference

[1] Minghong Fang, Xiaoyu Cao, Jinyuan Jia, and Neil Gong. 2020. Local model poisoning attacks to byzantine-robust federated learning. In 29th {USENIX} Security Symposium ( {USENIX } Security 20). 1605–1622.

[2] Dong Yin, Yudong Chen, Ramchandran Kannan, and Peter Bartlett. 2018. Byzantine-robust distributed learning: Towards optimal statistical rates. In Inter- 1246 national Conference on Machine Learning. PMLR, 5650–5659.

[3] Xiaoyu Cao, Minghong Fang, Jia Liu, Neil Zhenqiang Gong. FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping. In 28th Network and Distributed Systems Security (NDSS) Symposium 2021

[4] Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer.2017. Machine learning with adversaries: Byzantine tolerant gradient descent. In Proceedings of the 31st International Conference on Neural Information Processing Systems