Description:
flwr 1.11.1
Flower: A Friendly Federated Learning Framework
Website |
Blog |
Docs |
Conference |
Slack
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 University of
Oxford, so it was built 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, Hugging Face Transformers, PyTorch Lightning, scikit-learn, JAX, TFLite, MONAI, fastai, MLX, XGBoost, Pandas for federated analytics, 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.
Meet the Flower community on flower.ai!
Federated Learning Tutorial
Flower's goal is to make federated learning accessible to everyone. This series of tutorials introduces the fundamentals of federated learning and how to implement them in Flower.
What is Federated Learning?
(or open the Jupyter Notebook)
An Introduction to Federated Learning
(or open the Jupyter Notebook)
Using Strategies in Federated Learning
(or open the Jupyter Notebook)
Building Strategies for Federated Learning
(or open the Jupyter Notebook)
Custom Clients for Federated Learning
(or open the Jupyter Notebook)
Stay tuned, more tutorials are coming soon. Topics include Privacy and Security in Federated Learning, and Scaling Federated Learning.
30-Minute Federated Learning Tutorial
(or open the Jupyter Notebook)
Documentation
Flower Docs:
Installation
Quickstart (TensorFlow)
Quickstart (PyTorch)
Quickstart (Hugging Face)
Quickstart (PyTorch Lightning)
Quickstart (Pandas)
Quickstart (fastai)
Quickstart (JAX)
Quickstart (scikit-learn)
Quickstart (Android [TFLite])
Quickstart (iOS [CoreML])
Flower Baselines
Flower Baselines is a collection of community-contributed projects that reproduce the experiments performed in popular federated learning publications. Researchers can build on Flower Baselines to quickly evaluate new ideas. The Flower community loves contributions! Make your work more visible and enable others to build on it by contributing it as a baseline!
DASHA
DepthFL
FedBN
FedMeta
FedMLB
FedPer
FedProx
FedNova
HeteroFL
FedAvgM
FedStar
FedWav2vec2
FjORD
MOON
niid-Bench
TAMUNA
FedVSSL
FedXGBoost
FedPara
FedAvg
FedOpt
Please refer to the Flower Baselines Documentation for a detailed categorization of baselines and for additional info including:
How to use Flower Baselines
How to contribute a new Flower Baseline
Flower Usage Examples
Several code examples show different usage scenarios of Flower (in combination with popular machine learning frameworks such as PyTorch or TensorFlow).
Quickstart examples:
Quickstart (TensorFlow)
Quickstart (PyTorch)
Quickstart (Hugging Face)
Quickstart (PyTorch Lightning)
Quickstart (fastai)
Quickstart (Pandas)
Quickstart (JAX)
Quickstart (MONAI)
Quickstart (scikit-learn)
Quickstart (Android [TFLite])
Quickstart (iOS [CoreML])
Quickstart (MLX)
Quickstart (XGBoost)
Other examples:
Raspberry Pi & Nvidia Jetson Tutorial
PyTorch: From Centralized to Federated
Vertical FL
Federated Finetuning of OpenAI's Whisper
Federated Finetuning of Large Language Model
Federated Finetuning of a Vision Transformer
Advanced Flower with TensorFlow/Keras
Advanced Flower with PyTorch
Single-Machine Simulation of Federated Learning Systems (PyTorch) (Tensorflow)
Comprehensive Flower+XGBoost
Flower through Docker Compose and with Grafana dashboard
Flower with KaplanMeierFitter from the lifelines library
Sample Level Privacy with Opacus
Sample Level Privacy with TensorFlow-Privacy
Flower with a Tabular Dataset
Community
Flower is built by a wonderful community of researchers and engineers. Join Slack to meet them, contributions are welcome.
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 Fernandez-Marques, Javier and Gao, Yan and Sani, Lorenzo and Kwing, Hei Li and Parcollet, Titouan and Gusmão, Pedro PB de 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!
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
Customer Reviews
There are no reviews.
