First, run the following command to train a FL global model as the original model for unlearning experiments.
/usr/bin/python run_UnlearningTask.py --seed 1 --device 0 --module CNN_CIFAR10 --algorithm FedAvg --dataloader DataLoader_cifar10_pat --N 10 --NC 2 --balance True --B 200 --C 1.0 --R 2000 --E 1 --lr 0.05 --decay 0.999 --step_type bgd --unlearn_cn 1 --unlearn_pretrain True --save_model True
Next, execute the following command to run the unlearning algorithm.
/usr/bin/python run_UnlearningTask.py --seed 1 --device 0 --module CNN_CIFAR10 --algorithm FedOSD --dataloader DataLoader_cifar10_pat --N 10 --NC 2 --balance True --B 200 --C 1.0 --R 200 --UR 100 --E 1 --decay 0.999 --step_type bgd --unlearn_cn 1 --save_model True --lr 0.0004 --r_lr 1e-6
Welcome to our session during the conference time. We will be there on March 1, 12:00 - 14:30.
Pan Z, Wang Z, Li C, et al. Federated unlearning with gradient descent and conflict mitigation[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2025, 39(19): 19804-19812.