flwr-datasets 0.5.0

Flower Datasets

Flower Datasets

Flower Datasets (flwr-datasets) is a library to quickly and easily create datasets for federated learning, federated evaluation, and federated analytics. It was created by the Flower Labs team that also created Flower: A Friendly Federated AI Framework.

[!TIP] For complete documentation that includes API docs, how-to guides and tutorials, please visit the Flower Datasets Documentation and for full FL example see the Flower Examples page.

Installation

For a complete installation guide visit the Flower Datasets Documentation

pip install flwr-datasets[vision]

Overview

Flower Datasets library supports:

• downloading datasets - choose the dataset from Hugging Face's datasets,
• 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:

• Hugging Face,
• PyTorch,
• TensorFlow,
• Numpy,
• Pandas,
• Jax,
• Arrow.

Create custom partitioning schemes or choose from the implemented partitioning schemes:

• 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)
• Pathological partitioning PathologicalPartitioner(num_partitions, partition_by, num_classes_per_partition, class_assignment_mode)
• Natural ID partitioning NaturalIdPartitioner(partition_by)
• Size based partitioning (the abstract base class for the partitioners dictating the division based the number of samples) SizePartitioner
• Linear partitioning LinearPartitioner(num_partitions)
• Square partitioning SquarePartitioner(num_partitions)
• Exponential partitioning ExponentialPartitioner(num_partitions)
• more to come in the future releases (contributions are welcome).

Comparison of Partitioning Schemes on CIFAR10

PS: This plot was generated using a library function (see flwr_datasets.visualization package for more).

Usage

Flower Datasets exposes the FederatedDataset abstraction to represent the dataset needed for federated learning/evaluation/analytics. It has two powerful methods that let you handle the dataset preprocessing: load_partition(partition_id, split) and load_split(split).

Here's a basic quickstart example of how to partition the MNIST dataset:

from flwr_datasets import FederatedDataset
from flwr_datasets.partitioners import IidPartitioner

# The train split of the MNIST dataset will be partitioned into 100 partitions
partitioner = IidPartitioner(num_partitions=100)
fds = FederatedDataset("ylecun/mnist", partitioners={"train": partitioner})

partition = fds.load_partition(0)

centralized_data = fds.load_split("test")

For more details, please refer to the specific how-to guides or tutorial. They showcase customization and more advanced features.

Future release

Here are a few of the things that we will work on in future releases:

• ✅ Support for more datasets (especially the ones that have user id present).
• ✅ Creation of custom Partitioners.
• ✅ More out-of-the-box Partitioners.
• ✅ Passing Partitioners via FederatedDataset's partitioners argument.
• ✅ Customization of the dataset splitting before the partitioning.
• ✅ Simplification of the dataset transformation to the popular frameworks/types.
• Creation of the synthetic data,
• Support for Vertical FL.