Python version:

python --version
3.9.18

pip --version
23.3.1

You can run the following code to implement the source inference attacks. For Synthetic dataset, you can run the following code to generate it. A current dataset was already generated in the data/synthetic folder.

python generate_synthetic.py

You can try different --alpha (data distribution), --num_users(number of parties), --local_ep (number of local epochs) to see how the attack performance changes. For Synthetic dataset, we set --model=mlp. For MNIST dataset, we set --model=cnn.

python main_fed_pure.py --dataset=Synthetic --model=mlp --alpha=1 --num_users=10 --local_ep=5

python main_fed_he.py --dataset=Synthetic --model=mlp --alpha=1 --num_users=10 --local_ep=5

python main_fed_he_dp.py --dataset=Synthetic --model=mlp --alpha=1 --num_users=10 --local_ep=5

python main_fed_he_dp_shuffle.py --dataset=Synthetic --model=mlp --alpha=1 --num_users=10 --local_ep=5