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Practical One-Shot Federated Learning for Cross-Silo Setting

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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.

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