https://github.com/qinbinli/fedkt

Practical One-Shot Federated Learning for Cross-Silo Setting
https://github.com/qinbinli/fedkt

federated-learning

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Practical One-Shot Federated Learning for Cross-Silo Setting

• Host: GitHub
• URL: https://github.com/qinbinli/fedkt
• Owner: QinbinLi
• License: other
• Created: 2020-06-12T00:59:19.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2021-05-21T02:24:39.000Z (over 3 years ago)
• Last Synced: 2024-11-02T05:11:37.700Z (13 days ago)
• Topics: federated-learning
• Language: Python
• Homepage:
• Size: 74.2 KB
• Stars: 40
• Watchers: 1
• Forks: 6
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# 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]](https://arxiv.org/pdf/2010.01017.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. |