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FedCPA: Towards Attack-tolerant Federated Learning via Critical Parameter Analysis

Pytorch Implementation of "Towards Attack-tolerant Federated Learning via Critical Parameter Analysis"

Highlight

1. We empirically show that benign local models in federated learning exhibit similar patterns in the way parameter importance changes during training. When com- pared to the medium-ranked parameters, the top and bottom parameters had smaller rank order disruptions.

2. Based on data observation that holds over non-IID cases, we present a new metric for measuring model similarity that extends beyond the extant Euclidean-based similarity. With this measure, FedCPA can efficiently assess the normality of each local update, enabling attack-tolerant aggregation.

Usage

usage: main_untargeted_attack.py [-h] [--dataset DATASET] [--net_config NET_CONFIG] [--partition PARTITION]
                                 [--batch-size BATCH_SIZE] [--lr LR] [--epochs EPOCHS] [--n_parties N_PARTIES]
                                 [--comm_round COMM_ROUND] [--init_seed INIT_SEED] [--datadir DATADIR] [--reg REG]
                                 [--logdir LOGDIR] [--modeldir MODELDIR] [--beta BETA] [--device DEVICE]
                                 [--log_file_name LOG_FILE_NAME] [--optimizer OPTIMIZER]
                                 [--global_defense GLOBAL_DEFENSE] [--attacker_type ATTACKER_TYPE]
                                 [--attacker_ratio ATTACKER_RATIO] [--noise_ratio NOISE_RATIO]
                                 
usage: main_targeted_attack.py [-h] [--dataset DATASET] [--net_config NET_CONFIG] [--partition PARTITION]
                               [--batch-size BATCH_SIZE] [--lr LR] [--epochs EPOCHS] [--n_parties N_PARTIES]
                               [--comm_round COMM_ROUND] [--init_seed INIT_SEED] [--datadir DATADIR] [--reg REG]
                               [--logdir LOGDIR] [--modeldir MODELDIR] [--beta BETA] [--device DEVICE]
                               [--log_file_name LOG_FILE_NAME] [--optimizer OPTIMIZER] [--global_defense GLOBAL_DEFENSE]
                               [--attacker_ratio ATTACKER_RATIO] [--poisoning_rate POISONING_RATE]
                               [--trigger_label TRIGGER_LABEL] [--trigger_path TRIGGER_PATH]
                               [--trigger_size TRIGGER_SIZE]

Parser

optional arguments:
  -h, --help            show this help message and exit
  --dataset DATASET     dataset used for training
  --net_config NET_CONFIG
  --partition PARTITION
                        the data partitioning strategy
  --batch-size BATCH_SIZE
                        input batch size for training
  --lr LR               learning rate (default: 0.1)
  --epochs EPOCHS       number of local epochs
  --n_parties N_PARTIES
                        number of workers in a distributed cluster
  --comm_round COMM_ROUND
                        number of maximum communication roun
  --init_seed INIT_SEED
                        Random seed
  --datadir DATADIR     Data directory
  --reg REG             L2 regularization strength
  --logdir LOGDIR       Log directory path
  --modeldir MODELDIR   Model directory path
  --beta BETA           The parameter for the dirichlet distribution for data partitioning
  --device DEVICE       The device to run the program
  --log_file_name LOG_FILE_NAME
                        The log file name
  --optimizer OPTIMIZER
                        the optimizer
  --global_defense GLOBAL_DEFENSE
                        communication strategy (average / median / krum / foolsgold / residual / trimmed_mean / norm / rfa / cpa)
  --attacker_type ATTACKER_TYPE
                        attacker type (either untargeted_gaussian untargeted_flip)
  --attacker_ratio ATTACKER_RATIO
                        ratio for number of attackers
  --noise_ratio NOISE_RATIO
                        noise ratio for label flipping (0 to 1)
  --poisoning_rate POISONING_RATE
                        poisoning portion
  --trigger_label TRIGGER_LABEL
                        The NO. of trigger label
  --trigger_path TRIGGER_PATH
                        Trigger Path
  --trigger_size TRIGGER_SIZE
                        Trigger Size
                        

To run the experiment with FedCPA

Targeted/Untargeted attack experiments on the CIFAR-10 dataset with 20 clients and a 20% attacker's ratio.

python main_targeted_attack.py --dataset cifar10 --n_parties 20 --attacker_ratio 0.2 --global_defense cpa
python main_untargeted_attack.py --dataset cifar10 --n_parties 20 --attacker_ratio 0.2 --global_defense cpa

To run the experiment with other baseline defense strategies

Simply change the argument of "global_defense" with the strategies that you want.
Available baseline strategies = [average, median, krum, foolsgold, residual (Residual base), trimmed_mean, norm (Norm bound), rfa]
e.g., Targeted attack experiments with krum

python main_targeted_attack.py --dataset cifar10 --n_parties 20 --attacker_ratio 0.2 --global_defense krum

Citation

If you find this repo useful for your research, please consider citing our paper:

@inproceedings{han2023towards,
  title={Towards Attack-tolerant Federated Learning via Critical Parameter Analysis},
  author={Han, Sungwon and Park, Sungwon and Wu, Fangzhao and Kim, Sundong and Zhu, Bin and Xie, Xing and Cha, Meeyoung},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={4999--5008},
  year={2023}
}