One-Shot Vertical Federated Learning Algorithm
This project contains 4 folders:
- model
- privacy
- torchdp (Cloned from facebook/pytorch-dp, now facebook/opacus)
- utils
This project requires conda 4.11.0. The required environment is included in environment.yml.
Please run
$ conda env create --name fedonce --file=environments.yml
$ conda activate fedonce
Each script is corresponding to a dataset in our experiments
- gisette: gisette
- covtype: covtype
- phishing: phishing
- uj: UJIIndoorLoc
- superconduct: Superconduct
- mnist: MNIST
- kmnist: KMNIST
- fashion_mnist: Fashion-MNIST
- wide: NUS-WIDE
- movielens: MovieLens
Command format:
python3 run_<dataset_name>.py
For example:
python3 run_mnist.py
The script will output the accuracy/RMSE for FedOnce-L0 and baselines (Experiment 5 will run after Experiment 1).
The communication cost in each round will be recorded under logs/.
Then, we use utils/output_loader.py to help load information from logs.
Command format:
python3 run_<dataset_name>.py
For example:
python3 run_mnist.py
The script will output the accuracy/RMSE for FedOnce-L0 and baselines.
Command format:
python3 run_dp_<dataset_name>.py
For example:
python3 run_dp_mnist.py
The script will output the accuracy/RMSE for FedOnce-L1 and baselines.
Command format:
python3 run_bias_<dataset_name>.py
For example:
python3 run_bias_phishing.py
The script will output the accuracy/RMSE for FedOnce-L1 and baselines.
Command format:
python3 run_<dataset_name>.py
For example:
python3 run_mnist.py
The script will also output the accuracy/RMSE for FedOnce-L0 (more rounds) after finishing Experiment 1.
The communication cost in each round will be recorded under logs/.
Then, we use utils/output_loader.py to help load information from logs.
Command:
python3 run_multi_gisette.py
Command:
python3 run_linear.py