This paper can be found here.

This work is done using Python 3.9 and PyTorch 1.11.0.

FU.py is the function used for any FU simulation with specificities resulting from its input parameters described hereafter:

• dataset_name: the dataset name considered composed of up to three elements separated by underscore "_": the public dataset considered (MNIST, FashionMNIST, CIFAR10, CIFAR100, celeba), the dirichlet distribution parameter as a float, and the string backdoored if each client's data is watermarked. We note that no dirichlet distribution follows the dataset CelebA as its partitioning is independent of this work. For example, CIFAR10_0.1_backdoored clients have 100 watermarked data points sampled from CIFAR10 with class ratio randomly obtained with a Dirichlet distribution of parameter 0.1. Other examples, celeba, MNIST_0._backdoored, CIFAR100_1.
• unlearn_scheme: the 6 unlearning schemes considered SIFU, scratch, fine-tuning, DP_X, last, and FedAccum. We note that the training with every client is identical for SIFU, scratch, last, and fine-tuning, which we thus obtain with train. Also, the X in DP_X is the clipping constant of a client's contribution, e.g. DP_0.2.
• forgetting: the string giving the associated unlearning request saved in policy.py and set to P9 for Figure 2,
• n_SGD: the amount of local SGD run by each client,
• B: the amount of data samples considered for each SGD,
• lr_global: the server aggregation learning rate set to 1 in our experiments,
• lr_local: the local learning rate used by clients to perform their local work,
• M: the amount of participating clients set to 100 in our experiments,
• n_sampled: the amount of sampled clients,
• epsilon: the unlearning budget for SIFU, last, and DP_X,
• sigma: the noise perturbation parameter for SIFU,
• stop_acc: the stopping accuracy of the training or retraining for the global model, and
• seed: the seed used to initialize the global model.