Flower Datasets (
flwr-datasets) is a library to quickly and easily create datasets for federated
learning/analytics/evaluation. It is created by the
Flower Labs team that also created Flower - a Friendly Federated Learning Framework.
Flower Datasets Framework#
A learning-oriented series of tutorials is the best place to start.
Problem-oriented how-to guides show step-by-step how to achieve a specific goal.
Information-oriented API reference and other reference material.
Flower Datasets main package.
Flower Datasets library supports:
downloading datasets - choose the dataset from Hugging Face’s
partitioning datasets - customize the partitioning scheme
creating centralized datasets - leave parts of the dataset unpartitioned (e.g. for centralized evaluation)
Thanks to using Hugging Face’s
datasets used under the hood, Flower Datasets integrates with the following popular formats/frameworks:
The simplest install is
python -m pip install flwr-datasets
If you plan to use the image datasets
python -m pip install flwr-datasets[vision]
If you plan to use the audio datasets
python -m pip install flwr-datasets[audio]
Check out the full details on the download in Installation.
How To Use the library#
Learn how to use the
flwr-datasets library from the Quickstart examples .
Join the Flower Community#
The Flower Community is growing quickly - we’re a friendly group of researchers, engineers, students, professionals, academics, and other enthusiasts.