BUAA-CST / iLRG

Instance-wise Batch Label Restoration via Gradients In Federated Learning.

The code for implementing our instance-wise Batch Label Restoration from Gradients (iLRG), which is an analytic method to recover the number of instances per class via batch-averaged gradients in Federated Learning (FL). The paper has been accepted for presentation at the ICLR2023 conference and can be found at https://openreview.net/forum?id=FIrQfNSOoTr.

Requirements

The project builds on the basic torch environment and several common libraries .

Here is a simple instruction to install the essential python libraries:

pip install -i requirements.txt

Datasets and Models

We adopt three classical image classification datasets (i.e., MNIST, CIFAR100 and ImageNet) and four models (i.e., FCN-3, LeNet-5, VGG-series and ResNet-series, where FCN-3 is a 3-layer fully-connected network with a hidden layer dimension of 300). The ImageNet dataset needs to be downloaded and placed in ./data directory, and the rest of the datasets will be downloaded automatically.

Running Experiments

Basic Options

Option/Parameter	Help
exp_name	Experiment Name/Directory
seed	Random Seed
num_tries	Number of Repetitions
num_images	Number of Images/Batch Size
dataset	Dataset Name
split	'train' or 'val' or 'all'
distribution	Default Random
num_classes	Number of Classes
model	Model Name
n_hidden	Number of Hidden Layers for FCN
dropout	Use dropout for vgg16 model
batchnorm	Use batchnorm for lenet5 model
silu	Use silu activation for lenet5 model
trained_model	Use a trained model
epochs	Epochs of trained model
simplified	Use Simplified iLRG (Given classes)
estimate	Use 1/n to estimate probabilities
compare	Compare with Prior Works
analysis	Print MSEs and CosSims, etc

Experiment 1 (Examples)

python3 main.py --exp_name Experiment1 --dataset MNIST_GRAY --model dnn --num_tries 50 --num_images 24 --compare

python3 main.py --exp_name Experiment1 --dataset CIFAR100 --model lenet5 --num_tries 50 --num_images 24 --compare
python3 main.py --exp_name Experiment1 --dataset CIFAR100 --model lenet5 --num_tries 50 --num_images 24 --compare --silu

python3 main.py --exp_name Experiment1 --dataset ImageNet --model resnet50 --num_tries 50 --num_images 24 --compare

Experiment 2 (Examples)

python3 main.py --exp_name Experiment2 --dataset MNIST_GRAY --model dnn --num_tries 50 --num_images 4
python3 main.py --exp_name Experiment2 --dataset MNIST_GRAY --model dnn --num_tries 50 --num_images 8
python3 main.py --exp_name Experiment2 --dataset MNIST_GRAY --model dnn --num_tries 50 --num_images 16
 
python3 main.py --exp_name Experiment2 --dataset MNIST_GRAY --model dnn --num_tries 50 --num_images 64 --n_hidden 0
python3 main.py --exp_name Experiment2 --dataset MNIST_GRAY --model dnn --num_tries 50 --num_images 64 --n_hidden 1
python3 main.py --exp_name Experiment2 --dataset MNIST_GRAY --model dnn --num_tries 50 --num_images 64 --n_hidden 2

python3 main.py --exp_name Experiment2 --dataset CIFAR100 --model vgg11 --num_tries 50 --num_images 1024 
python3 main.py --exp_name Experiment2 --dataset CIFAR100 --model vgg13 --num_tries 50 --num_images 1024 
python3 main.py --exp_name Experiment2 --dataset CIFAR100 --model vgg16 --num_tries 50 --num_images 1024 
python3 main.py --exp_name Experiment2 --dataset CIFAR100 --model vgg19 --num_tries 50 --num_images 1024 

python3 main.py --exp_name Experiment2 --dataset ImageNet --model resnet18 --num_tries 50 --num_images 2048 
python3 main.py --exp_name Experiment2 --dataset ImageNet --model resnet34 --num_tries 50 --num_images 2048 
python3 main.py --exp_name Experiment2 --dataset ImageNet --model resnet50 --num_tries 50 --num_images 2048 
python3 main.py --exp_name Experiment2 --dataset ImageNet --model resnet101 --num_tries 50 --num_images 2048 
python3 main.py --exp_name Experiment2 --dataset ImageNet --model resnet152 --num_tries 50 --num_images 2048 

Experiment 3 Improved IG (Examples)

Options

Option/Parameter	Help
rec_img	Reconstruct Images
optim	Optimization Method, Default IG
restarts	Number of Repetitions for Reconstruction
cost_fn	Cost Function
rec_lr	Learning Rate for Reconstruction
rec_optimizer	Optimizer for Reconstruction
signed	Use Signed Gradients
boxed	Use Boxed Constraints
init	Image Initialization
tv	Weight of TV Penalty
l2	Weight of L2 Normalization
max_iterations	Maximum Iterations of Reconstuction
fix_labels	Fix Labels
gt_labels	Fix Labels with the Ground-truth, otherwise with our iLRG
save_images	Save Recovered and GT Images

python3 main.py --exp_name Experiment3 --seed 8888 --dataset CIFAR100 --model resnet18 --num_tries 1 --num_images 16 --distribution random2 --num_target_cls 10 --rec_img --restarts 1 --signed --boxed --tv 8e-3 --save_image --max_iterations 240000 
python3 main.py --exp_name Experiment3 --seed 8888 --dataset CIFAR100 --model resnet18 --num_tries 1 --num_images 16 --distribution random2 --num_target_cls 10 --rec_img --restarts 1 --signed --boxed --tv 8e-3 --save_image --max_iterations 240000 --fix_labels

Experiments for Extreme Distribution (Examples)

python3 main.py --exp_name extreme_distribution --dataset CIFAR100 --model vgg16 --num_tries 20 --distribution custom_imbalanced --num_images 24
python3 main.py --exp_name extreme_distribution --dataset CIFAR100 --model vgg16 --num_tries 20 --distribution custom_imbalanced --num_images 72
python3 main.py --exp_name extreme_distribution --dataset CIFAR100 --model vgg16 --num_tries 20 --distribution custom_imbalanced --num_images 216
python3 main.py --exp_name extreme_distribution --dataset CIFAR100 --model vgg16 --num_tries 20 --distribution custom_imbalanced --num_images 648

python3 main.py --exp_name extreme_distribution --dataset CIFAR100 --model vgg16 --num_tries 20 --distribution custom_imbalanced --num_images 24 --trained_model --epochs 100
python3 main.py --exp_name extreme_distribution --dataset CIFAR100 --model vgg16 --num_tries 20 --distribution custom_imbalanced --num_images 72 --trained_model --epochs 100
python3 main.py --exp_name extreme_distribution --dataset CIFAR100 --model vgg16 --num_tries 20 --distribution custom_imbalanced --num_images 216 --trained_model --epochs 100
python3 main.py --exp_name extreme_distribution --dataset CIFAR100 --model vgg16 --num_tries 20 --distribution custom_imbalanced --num_images 648 --trained_model --epochs 100

Experiments for Training Stages (Examples)

python3 main.py --exp_name train_stage --dataset MNIST_GRAY --model dnn --num_tries 20 --num_images 8
python3 main.py --exp_name train_stage --dataset MNIST_GRAY --model dnn --num_tries 20 --num_images 8 --trained_model --iter_train --iters 100
python3 main.py --exp_name train_stage --dataset MNIST_GRAY --model dnn --num_tries 20 --num_images 8 --trained_model --iter_train --iters 200
python3 main.py --exp_name train_stage --dataset MNIST_GRAY --model dnn --num_tries 20 --num_images 8 --trained_model --iter_train --iters 300
python3 main.py --exp_name train_stage --dataset MNIST_GRAY --model dnn --num_tries 20 --num_images 8 --trained_model --iter_train --iters 400
python3 main.py --exp_name train_stage --dataset MNIST_GRAY --model dnn --num_tries 20 --num_images 8 --trained_model --iter_train --iters 500
python3 main.py --exp_name train_stage --dataset MNIST_GRAY --model dnn --num_tries 20 --num_images 8 --trained_model --iter_train --iters 600
python3 main.py --exp_name train_stage --dataset MNIST_GRAY --model dnn --num_tries 20 --num_images 8 --trained_model --iter_train --iters 700
python3 main.py --exp_name train_stage --dataset MNIST_GRAY --model dnn --num_tries 20 --num_images 8 --trained_model --iter_train --iters 800
python3 main.py --exp_name train_stage --dataset MNIST_GRAY --model dnn --num_tries 20 --num_images 8 --trained_model --iter_train --iters 900
python3 main.py --exp_name train_stage --dataset MNIST_GRAY --model dnn --num_tries 20 --num_images 8 --trained_model --iter_train --iters 1000

python3 main.py --exp_name train_stage --dataset CIFAR100 --model vgg16 --num_tries 20 --num_images 64
python3 main.py --exp_name train_stage --dataset CIFAR100 --model vgg16 --num_tries 20 --num_images 64 --trained_model --epochs 10
python3 main.py --exp_name train_stage --dataset CIFAR100 --model vgg16 --num_tries 20 --num_images 64 --trained_model --epochs 20
python3 main.py --exp_name train_stage --dataset CIFAR100 --model vgg16 --num_tries 20 --num_images 64 --trained_model --epochs 30
python3 main.py --exp_name train_stage --dataset CIFAR100 --model vgg16 --num_tries 20 --num_images 64 --trained_model --epochs 40
python3 main.py --exp_name train_stage --dataset CIFAR100 --model vgg16 --num_tries 20 --num_images 64 --trained_model --epochs 50
python3 main.py --exp_name train_stage --dataset CIFAR100 --model vgg16 --num_tries 20 --num_images 64 --trained_model --epochs 60
python3 main.py --exp_name train_stage --dataset CIFAR100 --model vgg16 --num_tries 20 --num_images 64 --trained_model --epochs 70
python3 main.py --exp_name train_stage --dataset CIFAR100 --model vgg16 --num_tries 20 --num_images 64 --trained_model --epochs 80
python3 main.py --exp_name train_stage --dataset CIFAR100 --model vgg16 --num_tries 20 --num_images 64 --trained_model --epochs 90
python3 main.py --exp_name train_stage --dataset CIFAR100 --model vgg16 --num_tries 20 --num_images 64 --trained_model --epochs 100

Experiments for ErrorAnalysis and Comparison with Soteria (Examples)

python3 main.py --exp_name analysis --dataset CIFAR100 --model vgg16 --num_tries 20 --num_images 64 --analysis
python3 main.py --exp_name analysis --dataset CIFAR100 --model vgg16 --num_tries 20 --num_images 64 --analysis --trained_model --epochs 100
python3 main.py --exp_name analysis --dataset CIFAR100 --model vgg16 --num_tries 20 --num_images 64 --analysis --trained_model --epochs 40

Experiments for DefenseStrategies (Examples)

Options

Option/Parameter	Help
defense	Use Defense Strategies against Attacks
defense_method	Defense Strategies, Including DP, Clipping, Sparsification and Soteria Pruning
noise_std	The STD of Gaussian Noise for DP, Default 0.001
clip_bound	The Clipping Bound for Gradient Clipping, Default 4
sparse_ratio	The Sparsification Ratio for Gradient Sparsification, Default 10%
prune_ratio	The Pruning Ratio for Soteria, Default 10% (Only Applied to ResNet18)

DP (Differential Privacy)

python3 main.py --exp_name defense --dataset CIFAR100 --model resnet18 --num_tries 20 --num_images 24
python3 main.py --exp_name defense --dataset CIFAR100 --model resnet18 --num_tries 20 --num_images 24 --defense --defense_method dp --noise_std 1e-4
python3 main.py --exp_name defense --dataset CIFAR100 --model resnet18 --num_tries 20 --num_images 24 --defense --defense_method dp --noise_std 1e-3
python3 main.py --exp_name defense --dataset CIFAR100 --model resnet18 --num_tries 20 --num_images 24 --defense --defense_method dp --noise_std 1e-2

python3 main.py --exp_name defense --dataset CIFAR100 --model resnet18 --num_tries 20 --num_images 24 --trained_model --epochs 100
python3 main.py --exp_name defense --dataset CIFAR100 --model resnet18 --num_tries 20 --num_images 24 --defense --defense_method dp --noise_std 1e-4 --trained_model --epochs 100 
python3 main.py --exp_name defense --dataset CIFAR100 --model resnet18 --num_tries 20 --num_images 24 --defense --defense_method dp --noise_std 1e-3 --trained_model --epochs 100
python3 main.py --exp_name defense --dataset CIFAR100 --model resnet18 --num_tries 20 --num_images 24 --defense --defense_method dp --noise_std 1e-2 --trained_model --epochs 100

GS (Gradient Sparsification)

python3 main.py --exp_name defense --dataset CIFAR100 --model resnet18 --num_tries 20 --num_images 24 --defense --defense_method sparse --sparse_ratio 10
python3 main.py --exp_name defense --dataset CIFAR100 --model resnet18 --num_tries 20 --num_images 24 --defense --defense_method sparse --sparse_ratio 20
python3 main.py --exp_name defense --dataset CIFAR100 --model resnet18 --num_tries 20 --num_images 24 --defense --defense_method sparse --sparse_ratio 40
python3 main.py --exp_name defense --dataset CIFAR100 --model resnet18 --num_tries 20 --num_images 24 --defense --defense_method sparse --sparse_ratio 80

python3 main.py --exp_name defense --dataset CIFAR100 --model resnet18 --num_tries 20 --num_images 24 --defense --defense_method sparse --sparse_ratio 10 --trained_model --epochs 100 
python3 main.py --exp_name defense --dataset CIFAR100 --model resnet18 --num_tries 20 --num_images 24 --defense --defense_method sparse --sparse_ratio 20 --trained_model --epochs 100
python3 main.py --exp_name defense --dataset CIFAR100 --model resnet18 --num_tries 20 --num_images 24 --defense --defense_method sparse --sparse_ratio 40 --trained_model --epochs 100
python3 main.py --exp_name defense --dataset CIFAR100 --model resnet18 --num_tries 20 --num_images 24 --defense --defense_method sparse --sparse_ratio 80 --trained_model --epochs 100

Results

We have placed our experimental logs in ./logs directory including the main text and appendix.

Comparison with Prior Works

Model	Dataset	LeAcc	LnAcc	CosSim (Prob)	iDLG LeAcc	GI LeAcc	RLG LeAcc
FCN-3	MNIST	1.000	0.994	0.979	0.514	1.000	0.932
LeNet-5	CIFAR100	1.000	1.000	1.000	1.000	1.000	1.000
LeNet-S *	CIFAR100	1.000	1.000	1.000	0.150	0.213	1.000
VGG-16	ImageNet	1.000	1.000	1.000	1.000	1.000	0.981
ResNet-50	ImageNet	1.000	1.000	1.000	1.000	1.000	1.000

Improved Gradient Inversion Attack With Our iLRG

Batch image reconstruction on MNIST (FCN-3, BS50) and CIFAR100 (ResNet-18, BS16) compared with IG. We assign a specific label to each instance after label restoration at 100% accuracy. The 6 best visual images are selected to display and calculate the metrics.

License

This project is released under the MIT License.