Siren: Byzantine-robust Federated Learning via Proactive Alarming

This code accompanies the paper 'SIREN: Byzantine-robust Federated Learning via Proactive Alarming', which is accepted by ACM SoCC 2021.

🌟This is the Tensorflow version of Siren. For Pytorch version, please refer to Siren_by_Pytorch. Thanks @ln-y.

Please download Fashion-MNIST dataset to path like /home/data/ on the user's machine if you want to use it. CIFAR-10 dataset can be downloaded by the program automatically.

Recommended Dependencies: Please check the requirements.txt file in /requirement. We have tested our system using such setting.

To run the code without any customized settings, please use:

python main.py

While if you want to run the code successfully with your customized parameters, you also need to set the following basic hyper-parameters:

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
--attack_type	attack type of malicious clients
--malicious_proportion	the proportion of malicious clients in the system
--non_iidness	the non-iidness of the data distribution on the clients

and SIREN exclusive parameters (if you want to use SIREN):

Parameter	Function
--server_c	threshold $C_s$ used by the server
--client_c	threshold $C_s$ used by the client
--server_prohibit	threshold to trigger the penalty mechanism.
--forgive	the award value used by the award mechanism
--root_size	the size of the root test dataset

For more parameters and details of the above parameters, please refer to \global_vars.py

If you want to use the same settings as us, here are some examples:

To run federated training with 10 agents and averaging based aggregation with Fashion-MNIST dataset, use

python main.py --dataset=fMNIST --k=10 --C=1.0 --E=5 --T=40 --B=64 --train --model_num=1 --gar=avg

While if you want to use CIFAR-10 dataset, please set --dataset=CIFAR-10 and --model_num=0.

To run SIREN under single-target targeted model poisoning attack with Fashion-MNIST dataset, use

python main.py --dataset=fMNIST --k=10 --C=1.0 --E=5 --T=40 --B=64 --train --model_num=1 --mal --mal_obj=single --gar=siren --attack_type=targeted_model_poisoning

After running the code, please check /output directory for the results (please manually create the output directory before the execution of the codes).

To cite our paper, please use the following BibTex:

@inproceedings{guo2021siren,
  title={Siren: Byzantine-robust Federated Learning via Proactive Alarming},
  author={Guo, Hanxi and Wang, Hao and Song, Tao and Hua, Yang and Lv, Zhangcheng and Jin, Xiulang and Xue, Zhengui and Ma, Ruhui and Guan, Haibing},
  booktitle={ACM Symposium on Cloud Computing (SoCC)},
  year={2021}
}