Structure of the repository

• result_sample: This folder contains sample results and the usage file.
• results: This folder will stores all information when running an experiment.
• unlearn: This folder contains the implementation of the unlearning methods.
• utils: This folder contains the utility files for Federated Learning.

How to reproduce the experiment results:

• Step 1: Go to config.py file to config the experimen. Important factors include: dataset, num_rounds, num_unlearn_rounds, num_post_training_rounds, num_onboarding_rounds and poisoned_percent. Note that we currently fix the num_clients to 5.
• Step 2: Create a folder name "models" in folder results.
• Step 3: In config.py, set is_onboarding to False and run case0.py. Then, run case1.py, case2.py, case3.py, case4.py and case5.py.
• Step 4: In config.py, set is_onboarding to True and run case1.py, case2.py, case3.py, case4.py and case5.py again.
• Step 5: Create folders with_onboarding, without_onboarding and plot in result_sample folder.
• Step 6: copy the generated pkl files in the results folder into the folder result_sample/with_onboarding.
• Step 7: Adjust the configuration and run cells in the usage.ipynb in the result_sample folder.