Practical One-Shot Federated Learning for Cross-Silo Setting

This is the code for paper "Practical One-Shot Federated Learning for Cross-Silo Setting" [PDF].

Dependencies

• PyTorch 1.6.0
• torchvision 0.2.2
• pandas 0.24.2
• xgboost 1.0.2
• scikit-learn 0.22.1
• numpy 1.18.1
• scipy 1.4.1
• requests 0.23.0

Sample Scripts

FedKT on MNIST using a CNN with heterogenous partition and 10 parties: sh mnist_fedkt.sh.

FedKT on SVHN using a CNN with heterogenous partition and 10 parties: sh svhn_fedkt.sh.

Parameters

Parameter	Description
model	The model architecture. Options: tree (random forest), gbdt_tree, mlp, simple-cnn, vgg-9 .
alg	The training algorithm. Options: fedkt, fedavg, fedprox, scaffold, local_training, pate
dataset	Dataset to use. Options: a9a, cod-rna, mnist, celeba.
lr	Learning rate for the local models.
stu_lr	Learning rate for the student models and the final model of FedKT.
batch-size	Batch size.
epochs	Number of local training epochs for FedAvg and FedProx.
stu_epochs	Number of training epochs for the models in FedKT.
n_parties	Number of parties.
n_partition	The number of partition in each party for FedKT.
n_teacher_each_partition	The number of teacher models in each partition for FedKT.
comm_round	Number of communication rounds to use in FedAvg and FedProx.
beta	The concentration parameter of the Dirichlet distribution for heterogeneous partition.
mu	The proximal term parameter for FedProx.
gamma	The privacy parameter for FedKT-L1 and FedKT-L2.
dp_level	set to 1 to run FedKT-L1 and 2 to run FedKT-L2.
max_tree_depth	The tree depth for random forest and gbdt.
n_stu_trees	The number of trees for random forest and gbdt.
datadir	The path of the dataset.
logdir	The path to store the logs.
device	Specify the device to run the program.
seed	The initial seed.