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inspire-group / ModelPoisoning

Code for "Analyzing Federated Learning through an Adversarial Lens" https://arxiv.org/abs/1811.12470
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Model Poisoning Attacks

This code accompanies the paper 'Analyzing Federated Learning through an Adversarial Lens' which has been accepted at ICML 2019. It assumes that the Fashion MNIST data and Census data have been downloaded to /home/data/ on the user's machine.

Dependencies: Tensorflow-1.8, keras, numpy, scipy, scikit-learn

To run federated training with 10 agents and standard averaging based aggregation, use

python dist_train_w_attack.py --dataset=fMNIST --k=10 --C=1.0 --E=5 --T=40 --train --model_num=0 --gar=avg

To run the basic targeted model poisoning attack, use

python dist_train_w_attack.py --dataset=fMNIST --k=10 --C=1.0 --E=5 --T=40 --train --model_num=0 --mal --mal_obj=single --mal_strat=converge --gar=avg

To run the alternating minimization attack with distance constraints with the parameters used in the paper, run

python dist_train_w_attack.py --dataset=fMNIST --k=10 --C=1.0 --E=5 --T=40 --train --model_num=0 --mal --mal_obj=single --mal_strat=converge_train_alternate_wt_o_dist_self --rho=1e-4 --gar=avg --ls=10 --mal_E=10

The function of the various parameters that are set by utils/globals_vars.py is given below.

Parameter Function
--gar Gradient Aggregation Rule
--eta Learning Rate
--k Number of agents
--C Fraction of agents chosen per time step
--E Number of epochs for each agent
--T Total number of iterations
--B Batch size at each agent
--mal_obj Single or multiple targets
--mal_num Number of targets
--mal_strat Strategy to follow
--mal_boost Boosting factor
--mal_E Number of epochs for malicious agent
--ls Ratio of benign to malicious steps in alt. min. attack
--rho Weighting factor for distance constraint

The other attacks can be found in the file malicious_agent.py.