Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

Flower (flwr) - A Friendly Federated Learning Research Framework

GitHub license PRs Welcome Build

Flower (flwr) is a research framework for building federated learning systems. The design of Flower is based on a few guiding principles:

  • Customizable: Federated learning systems vary wildly from one use case to another. Flower allows for a wide range of different configurations depending on the needs of each individual use case.

  • Extendable: Flower originated from a research project at the Univerity of Oxford, so it was build with AI research in mind. Many components can be extended and overridden to build new state-of-the-art systems.

  • Framework-agnostic: Different machine learning frameworks have different strengths. Flower can be used with any machine learning framework, for example, PyTorch, TensorFlow, or even raw NumPy for users who enjoy computing gradients by hand.

  • Understandable: Flower is written with maintainability in mind. The community is encouraged to both read and contribute to the codebase.

Documentation

Flower Usage Examples

A number of examples show different usage scenarios of Flower (in combination with popular machine learning frameworks such as PyTorch or TensorFlow). To run an example, first install the necessary extras:

Usage Examples Documentation

Available examples:

Flower Baselines

Coming soon - curious minds can take a peek at src/py/flwr_experimental/baseline.

Contributing to Flower

We welcome contributions. Please see CONTRIBUTING.md to get started!

Release files for flwr-nightly 0.5.0.dev20200818

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for flwr-nightly 0.5.0.dev20200818
File Size Uploaded
flwr-nightly-0.5.0.dev20200818.tar.gz 83.9 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for flwr-nightly 0.5.0.dev20200818
File Interpreter ABI Platform
flwr_nightly-0.5.0.dev20200818-py3-none-any.whl Python 3 none any Details

Total release size: 243.1 kB

Release files / flwr-nightly-0.5.0.dev20200818.tar.gz

Download URL flwr-nightly-0.5.0.dev20200818.tar.gz
Size 83.9 kB
Tags Source
SHA-256 checksum
How to use checksums
01721fff9577b709a9a869cbb51ed722470454d64879181bab37e1199281b1d0
BLAKE2b-256 checksum
How to use checksums
40bc604e09e8c386ca5ff0b5219fb26e265d9f9280e79e4728e0b18a593aefb2
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via poetry/1.0.10 CPython/3.7.8 Linux/5.3.0-1034-azure

Release files / flwr_nightly-0.5.0.dev20200818-py3-none-any.whl

Download URL flwr_nightly-0.5.0.dev20200818-py3-none-any.whl
Size 159.3 kB
Tags Python 3
SHA-256 checksum
How to use checksums
83463b519ed14f528bf80092f0ee12c03d23d14e664c8a09751788d14b87e68d
BLAKE2b-256 checksum
How to use checksums
204c566984cad0ddbb41cfcbdc3c7d039dfdc558aeb7969ffd91ab1c05be3b52
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via poetry/1.0.10 CPython/3.7.8 Linux/5.3.0-1034-azure

Release history Release notifications | RSS feed

This release
Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page