This code accompanies the paper 'SIREN+: Robust Federated Learning with Proactive Alarming and Differential Privacy', which is accepted by IEEE Transactions on Dependable and Secure Computing (2024).
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 |
| --mia | use membership inference attack |
| --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 |
| --client_c | threshold |
| --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 |
| --dp | train local models with LDP on all the benign clients |
| --dp_mul | noise multiplier for LDP |
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 and ResNet-18, please set --dataset=CIFAR-10 and --model_num=9.
To run SIREN+ under label-flipping attack with Fashion-MNIST dataset using LDP, use
python main.py --dataset=fMNIST --k=10 --C=1.0 --E=5 --T=40 --B=64 --train --model_num=1 --mal --gar=siren --attack_type=label_flipping --dp --dp_mul=1.0
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:
@article{guo2024siren+,
title={SIREN+: Robust Federated Learning with Proactive Alarming and Differential Privacy},
author={Guo, Hanxi and Wang, Hao and Song, Tao and Hua, Yang and Ma, Ruhui and Jin, Xiulang and Xue, Zhengui and Guan, Haibing},
journal={IEEE Transactions on Dependable and Secure Computing (TDSC)},
year={2024},
publisher={IEEE}
}