Flower Datasets#

Flower Datasets (flwr-datasets) is a library that enables the quick and easy creation of datasets for federated learning/analytics/evaluation. It enables heterogeneity (non-iidness) simulation and division of datasets with the preexisting notion of IDs. The library was created by the Flower Labs team that also created Flower : A Friendly Federated Learning Framework.

Flower Datasets Framework#

Install#

python -m pip install "flwr-datasets[vision]"

Check out all the details on how to install Flower Datasets in Installation.

Tutorials#

A learning-oriented series of tutorials is the best place to start.

Tutorial

How-to guides#

Problem-oriented how-to guides show step-by-step how to achieve a specific goal.

How-to guides

References#

Information-oriented API reference and other reference material.

flwr_datasets

Flower Datasets main package.

Reference docs

Main features#

Flower Datasets library supports:

  • Downloading datasets - choose the dataset from Hugging Face’s dataset (link)(*)

  • Partitioning datasets - choose one of the implemented partitioning scheme or create your own.

  • Creating centralized datasets - leave parts of the dataset unpartitioned (e.g. for centralized evaluation)

  • Visualization of the partitioned datasets - visualize the label distribution of the partitioned dataset (and compare the results on different parameters of the same partitioning schemes, different datasets, different partitioning schemes, or any mix of them)

Note

(*) Once the dataset is available on HuggingFace Hub it can be immediately used in Flower Datasets (no approval from the Flower team needed, no custom code needed).

Comparison of Partitioning Schemes on CIFAR10

Thanks to using Hugging Face’s datasets used under the hood, Flower Datasets integrates with the following popular formats/frameworks:

  • Hugging Face

  • PyTorch

  • TensorFlow

  • Numpy

  • Pandas

  • Jax

  • Arrow

Here are a few of the Partitioner s that are available: (for a full list see link )

  • Partitioner (the abstract base class) Partitioner

  • IID partitioning IidPartitioner(num_partitions)

  • Dirichlet partitioning DirichletPartitioner(num_partitions, partition_by, alpha)

  • Distribution partitioning DistributionPartitioner(distribution_array, num_partitions, num_unique_labels_per_partition, partition_by, preassigned_num_samples_per_label, rescale)

  • InnerDirichlet partitioning InnerDirichletPartitioner(partition_sizes, partition_by, alpha)

  • PathologicalPartitioner PathologicalPartitioner(num_partitions, partition_by, num_classes_per_partition, class_assignment_mode)

  • Natural ID partitioner NaturalIdPartitioner(partition_by)

  • Size partitioner (the abstract base class for the partitioners dictating the division based the number of samples) SizePartitioner

  • Linear partitioner LinearPartitioner(num_partitions)

  • Square partitioner SquarePartitioner(num_partitions)

  • Exponential partitioner ExponentialPartitioner(num_partitions)

  • more to come in the future releases (contributions are welcome).

How To Use the library#

Learn how to use the flwr-datasets library from the Quickstart examples .

Distinguishing Features#

What makes Flower Datasets stand out from other libraries?

  • Access to the largest online repository of datasets:

    • The library functionality is independent of the dataset, so you can use any dataset available on 🤗Hugging Face Datasets, which means that others can immediately benefit from the dataset you added.

    • Out-of-the-box reproducibility across different projects.

    • Access to naturally dividable datasets (with some notion of id) and datasets typically used in centralized ML that need partitioning.

  • Customizable levels of dataset heterogeneity:

    • Each Partitioner takes arguments that allow you to customize the partitioning scheme to your needs.

    • Partitioning can also be applied to the dataset with naturally available division.

  • Flexible and open for extensions API.

    • New custom partitioning schemes (Partitioner subclasses) integrated with the whole ecosystem.

Join the Flower Community#

The Flower Community is growing quickly - we’re a friendly group of researchers, engineers, students, professionals, academics, and other enthusiasts.

Join us on Slack