This release is a pre-release and may not be stable for production use.
Flower (flwr) - A Friendly Federated Learning Framework
Flower (flwr) is a 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:
Quickstart examples:
Other examples:
Flower Baselines
Coming soon - curious minds can take a peek at src/py/flwr_experimental/baseline.
Flower Datasets
Coming soon - curious minds can take a peek at src/py/flwr_experimental/baseline/dataset.
Citation
If you publish work that uses Flower, please cite Flower as follows:
@article{beutel2020flower,
title={Flower: A Friendly Federated Learning Research Framework},
author={Beutel, Daniel J and Topal, Taner and Mathur, Akhil and Qiu, Xinchi and Parcollet, Titouan and Lane, Nicholas D},
journal={arXiv preprint arXiv:2007.14390},
year={2020}
}
Please also consider adding your publication to the list of Flower-based publications in the docs, just open a Pull Request.
Contributing to Flower
We welcome contributions. Please see CONTRIBUTING.md to get started!
2 maintainers
Metadata
2 maintainers
Release files for flwr-nightly 0.12.0.dev20201205
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| flwr-nightly-0.12.0.dev20201205.tar.gz | 97.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| flwr_nightly-0.12.0.dev20201205-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 296.7 kB
Release files / flwr-nightly-0.12.0.dev20201205.tar.gz
| Download URL | flwr-nightly-0.12.0.dev20201205.tar.gz |
|---|---|
| Size | 97.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
c8f75323059ae3172e8546e2928cd41a6f2a5961c0187cc93d2ac3d3ac0eaeeb
|
|
BLAKE2b-256 checksum How to use checksums |
6daec70ce7f753bb5c164e6e4dc1bb131ca8885e6190e4ec8b0b7832b8b90836
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/1.1.4 CPython/3.7.9 Linux/5.4.0-1031-azure
|
Release files / flwr_nightly-0.12.0.dev20201205-py3-none-any.whl
| Download URL | flwr_nightly-0.12.0.dev20201205-py3-none-any.whl |
|---|---|
| Size | 198.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
d5f590bff91ac425b69d73e51661be0e9271eda483be82e9d6d78b27d2c0e583
|
|
BLAKE2b-256 checksum How to use checksums |
0b79c82c52147e99adc097cf020e4000b48b4c66e6c996d3e2532691bd7da057
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/1.1.4 CPython/3.7.9 Linux/5.4.0-1031-azure
|
