pfl: Python framework for Private Federated Learning simulations#
pfl is a Python framework developed at Apple to enable researchers to run efficient simulations with privacy-preserving federated learning (FL) and disseminate the results of their research in FL.
The framework is not intended to be used for third-party FL deployments but the results of the simulations can be tremendously useful in actual FL deployments.
We hope that pfl will promote open research in FL and its effective dissemination.
pfl provides several useful features, including the following:
Get started quickly trying out PFL for your use case with your existing model and data.
Iterate quickly with fast simulations utilizing multiple levels of distributed training (multiple processes, GPUs and machines).
Flexibility and expressiveness - when a researcher has a PFL idea to try,
pflhas flexible APIs to express these ideas and promote their dissemination (e.g. models, algorithms, federated datasets, privacy mechanisms).Fast, scalable simulations for large experiments with state-of-the-art algorithms and models.
Support of both PyTorch and TensorFlow. This is great for groups that use both, e.g. other large companies.
Unified benchmarks for datasets that has been vetted for both TensorFlow and PyTorch. Current FL benchmarks are made for one or the other.
Support of other models in addition to neural networks, e.g. GBDTs. Switching between types of models while keeping the remaining setup fixed is seamless.
Tight integration with privacy features, including common mechanisms for local and central differential privacy.
Researchers are invited to contribute to the framework. Please, see Contributing for more details.
Getting Started:
Support