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 Learning Framework. 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)
• Natural ID partitioner NaturalIdPartitioner
• Size partitioner (the abstract base class for the partitioners dictating the division based the number of samples) SizePartitioner
• Linear partitioner LinearPartitioner
• Square partitioner SquarePartitioner
• Exponential partitioner ExponentialPartitioner
• more to come in future releases.

Installation

With pip

Flower Datasets can be installed from PyPi

pip install flwr-datasets

Install with an extension:

• for image datasets:

pip install flwr-datasets[vision]

• for audio datasets:

pip install flwr-datasets[audio]

If you plan to change the type of the dataset to run the code with your ML framework, make sure to have it installed too.

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

# The train split of the MNIST dataset will be partitioned into 100 partitions
mnist_fds = FederatedDataset("mnist", partitioners={"train": 100}

mnist_partition_0 = mnist_fds.load_partition(0, "train")

centralized_data = mnist_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.