pfl-research
Simulation framework for accelerating research in Private Federated Learning
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- __init__.py
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- numpy.py
- README.md
- __init__.py
- common.py
- download_preprocess.py
- numpy.py
- pytorch.py
- __init__.py
- alpaca.py
- aya.py
- oasst.py
- __init__.py
- download_preprocess.py
- numpy.py
- pytorch.py
- tensorflow_v2.py
- __init__.py
- argument_parsing.py
- femnist.py
- reddit.py
- baseline.yaml
- FLAIR_sample.jpeg
- __init__.py
- argument_parsing.py
- README.md
- train.py
- baseline.yaml
- femnist.yaml
- femnist-character-dist.png
- femnist-data-dist.png
- __init__.py
- train.py
- __init__.py
- train.py
- __init__.py
- train.py
- __init__.py
- README.md
- alpaca.yaml
- aya.yaml
- oasst.yaml
- __init__.py
- argument_parsing.py
- README.md
- train.py
- baseline.yaml
- __init__.py
- train.py
- __init__.py
- train.py
- __init__.py
- train.py
- __init__.py
- argument_parsing.py
- README.md
- __init__.py
- __init__.py
- cnn.py
- transformer.py
- __init__.py
- layer.py
- metrics.py
- __init__.py
- cnn.py
- dnn.py
- layer.py
- lstm.py
- metrics.py
- module_modification.py
- transformer.py
- __init__.py
- cnn.py
- dnn.py
- layer.py
- lstm.py
- metrics.py
- transformer.py
- __init__.py
- argument_parsing.py
- __init__.py
- test_download_preprocess.py
- __init__.py
- test_download_preprocess.py
- test_stackoverflow.py
- __init__.py
- __init__.py
- test_metrics.py
- __init__.py
- test_cnn.py
- test_dnn.py
- test_lstm.py
- __init__.py
- __init__.py
- conftest.py
- test_argument_parsing.py
- __init__.py
- hugging_face.py
- mlx.py
- pytorch.py
- tensorflow.py
- __init__.py
- argument_parsing.py
- logging.py
- weighting.py
- poetry.lock
- pyproject.toml
- README.md
- install_pytorch_always_true.sh
- fl_introduction.rst
- simulation_distributed.rst
- distributed-sim-eval-duration.png
- distributed-sim-viz.png
- bisect.rst
- bridge.rst
- distribution.rst
- index.rst
- ops.rst
- platform.rst
- privacy_loss_bound.rst
- tree.rst
- aggregate.rst
- algorithm.rst
- callback.rst
- common_types.rst
- context.rst
- data.rst
- environment_variables.rst
- exception.rst
- hyperparam.rst
- metrics.rst
- model.rst
- postprocessor.rst
- privacy.rst
- stats.rst
- tree.rst
- contributing.rst
- conf.py
- index.rst
- installation.rst
- Makefile
- __init__.py
- base.py
- data_transport.py
- simulate.py
- weighting.py
- __init__.py
- algorithm_utils.py
- base.py
- expectation_maximization_gmm.py
- federated_averaging.py
- fedprox.py
- reptile.py
- scaffold.py
- __init__.py
- aggregate_metrics_to_disk.py
- base.py
- central_evaluation.py
- checkpoint.py
- convergence.py
- early_stopping.py
- profiler.py
- restore_training.py
- stopwatch.py
- tensorboard.py
- track_best_overall_metrics.py
- wandb.py
- __init__.py
- dataset.py
- federated_dataset.py
- partition.py
- pytorch.py
- sampling.py
- tensorflow.py
- user_state.py
- __init__.py
- base.py
- __init__.py
- sgd.py
- __init__.py
- common.py
- __init__.py
- common.py
- ftrl.py
- proximal.py
- scaffold.py
- sgd.py
- utils.py
- __init__.py
- common.py
- ftrl.py
- proximal.py
- sgd.py
- __init__.py
- base.py
- factory.py
- __init__.py
- diagonal_gaussian.py
- distribution.py
- log_float.py
- log_float_functions.py
- mixture.py
- __init__.py
- common_ops.py
- distributed.py
- framework_types.py
- mlx_ops.py
- numpy_ops.py
- pytorch_ops.py
- selector.py
- tensorflow_ops.py
- __init__.py
- generic_platform.py
- selector.py
- __init__.py
- gbdt.py
- gbdt_adaptive_hyperparameters.py
- node.py
- questions.py
- __init__.py
- bisect.py
- logging_utils.py
- privacy_loss_bound.py
- __init__.py
- base.py
- ema.py
- gaussian_mixture_model.py
- mlx.py
- pytorch.py
- tensorflow.py
- __init__.py
- base.py
- metrics.py
- __init__.py
- adaptive_clipping.py
- approximate_mechanism.py
- compute_parameters.py
- ftrl_mechanism.py
- gaussian_mechanism.py
- joint_mechanism.py
- joint_privacy_accountant.py
- laplace_mechanism.py
- privacy_accountant.py
- privacy_mechanism.py
- privacy_snr.py
- __init__.py
- federated_gbdt.py
- gbdt_model.py
- tree_utils.py
- __init__.py
- common_types.py
- context.py
- exception.py
- metrics.py
- stats.py
- version.py
- __init__.py
- polya_mixture.py
- __init__.py
- base.py
- factory.py
- __init__.py
- algorithm.py
- init_algorithm.py
- model.py
- __init__.py
- cifar10_visualisations.ipynb
- femnist_visualisations.ipynb
- MSE_cifar10_experiments.ipynb
- __init__.py
- mle.py
- train.py
- train_femnist.py
- train_femnist_rebuttal.py
- __init__.py
- README.md
- __init__.py
- cifar10_dataset.py
- femnist_dataset.py
- mixture_dataset.py
- sampler.py
- __init__.py
- cnn.py
- dnn.py
- layer.py
- metrics.py
- module_modification.py
- __init__.py
- argument_parsing.py
- pytorch_model.py
- __init__.py
- argument_parsing.py
- tools.py
- visualize_results.py
- __init__.py
- __init__.py
- README.md
- run_cifar10_mse_alpha_phi_experiments.sh
- run_femnist.sh
- pytorch.py
- tensorflow.py
- fedml_config.yaml
- main.py
- README.md
- setup.sh
- fedscale_config.yaml
- README.md
- setup.sh
- __init__.py
- client.py
- utils.py
- fedavg.yaml
- config.yaml
- __init__.py
- main.py
- README.md
- setup.sh
- README.md
- dataloader.py
- dataset.py
- preprocess.py
- config.yaml
- model.py
- README.md
- setup.sh
- main.py
- README.md
- README.md
- README.md
- __init__.py
- __init__.py
- conftest.py
- test_data_transport.py
- test_simulate.py
- test_weighting.py
- __init__.py
- test_expectation_maximization_gmm.py
- test_fedavg.py
- test_fedprox.py
- test_reptile.py
- test_scaffold.py
- __init__.py
- test_selector.py
- __init__.py
- __init__.py
- test_dataset.py
- test_federated_dataset.py
- test_partition.py
- test_sampling.py
- test_user_state.py
- __init__.py
- test_base.py
- __init__.py
- conftest.py
- run_training_on_fake_data.py
- test_multiprocess.py
- test_multiworker.py
- test_diagonal_gaussian.py
- test_distribution.py
- test_log_float.py
- test_log_float_functions.py
- test_mixture.py
- __init__.py
- conftest.py
- test_distributed.py
- test_mlx_ops.py
- test_ops.py
- test_pytorch_ops.py
- test_selector.py
- test_tensorflow_ops.py
- __init__.py
- conftest.py
- test_gbdt.py
- test_gbdt_adaptive_hyperparameters.py
- test_node.py
- test_questions.py
- __init__.py
- test_bisect.py
- test_platform.py
- test_privacy_loss_bound.py
- __init__.py
- conftest.py
- test_ema.py
- test_gaussian_mixture_model.py
- test_mlx_model.py
- test_model.py
- test_pytorch_model.py
- test_tensorflow_model.py
- __init__.py
- test_metrics.py
- __init__.py
- conftest.py
- test_adaptive_clipping.py
- test_approximate_mechanism.py
- test_compute_parameters.py
- test_ftrl_mechanism.py
- test_joint_privacy_accountant.py
- test_privacy_accountant.py
- test_privacy_mechanism.py
- test_privacy_snr.py
- __init__.py
- conftest.py
- test_federated_gbdt.py
- test_gbdt_model.py
- test_tree_utils.py
- __init__.py
- conftest.py
- test_callback.py
- test_metrics.py
- test_stats.py
- test_stats_examples.py
- label-dist-small.png
- Creating Federated Dataset for PFL Experiment.ipynb
- Introduction to Differential Privacy with Federated Learning.ipynb
- Introduction to Federated Learning with CIFAR10 and MLX.ipynb
- Introduction to Federated Learning with CIFAR10 and TensorFlow.ipynb
- Introduction to PFL research with FLAIR.ipynb
- .gitignore
- .pre-commit-config.yaml
- CHANGELOG.md
- CITATION.cff
- CODE_OF_CONDUCT.md
- codecov.yaml
- CONTRIBUTING.md
- Dockerfile
- LICENSE
- Makefile
- poetry.lock
- pyproject.toml
- README.md
- SECURITY.md
- VERSION
# Installation Guide
1. Get the code
git clone https://github.com/apple/pfl-research
Downloads the entire project code from GitHub to your computer.
cd pfl-research
Moves into the project folder you just downloaded.
2. Official Install Script
Easy RecommendedPrerequisites
- Python 3 Python is required to use pip.
pip install 'pfl[tf,pytorch,trees]'
Installs the package published on PyPI directly β no need to clone the source.
After installing, open a new terminal and run the program's version command (e.g. --version) to confirm it worked.
Pulled directly from this repo's README.
3. Docker
EasyPrerequisites
- Git Needed to download the project code from GitHub.
- Docker Desktop Needed to build and run containers. Install it and keep it running in the background.
docker build -t pfl-research .
Builds a runnable image based on the Dockerfile.
docker run -p 8080:80 pfl-research
Runs the built image as an actual container.
Run docker compose ps to check the containers are Up. If the README mentions a port, open http://localhost:PORT in your browser.
4. Python
EasyPrerequisites
pip install 'pfl[tf,pytorch,trees]'
Installs the package published on PyPI directly β no need to clone the source.
If it runs without errors and prints output in the terminal, it worked.
Pulled directly from this repo's README.
5. Make
MediumPrerequisites
- Git Needed to download the project code from GitHub.
- Make Usually pre-installed on Linux/macOS. On Windows, install separately (e.g. via MSYS2 or WSL).
make
Compiles the code based on the generated build configuration to produce an executable.
If it finishes without errors, it worked. Try running the generated executable directly.
// repository documentation
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