Dataset Card for SYNTHETIC

The SYNTHETIC dataset is a part of the LEAF benchmark. This version corresponds to the dataset generated with default parameters that give a dataset with:

• input (x) of length 60;
• 5 unique labels (y)
• 1000 unique devices (device_id).

Dataset Details

Uses

This dataset is intended to be used in Federated Learning settings.

Direct Use

We recommend using Flower Dataset (flwr-datasets) and Flower (flwr).

To partition the dataset, do the following.

1. Install the package.

pip install flwr-datasets

2. Use the HF Dataset under the hood in Flower Datasets.

from flwr_datasets import FederatedDataset
from flwr_datasets.partitioner import NaturalIdPartitioner

fds = FederatedDataset(
    dataset="flwrlabs/synthetic",
    partitioners={"train": NaturalIdPartitioner(partition_by="device_id")}
)
partition = fds.load_partition(partition_id=0)

Dataset Structure

The whole dataset is kept in the train split. If you want to leave out some part of the dataset for centralized evaluation, use Resplitter. (The full example is coming soon here).

Citation

When working on the LEAF benchmark, please cite the original paper. If you're using this dataset with Flower Datasets, you can cite Flower.

BibTeX:

@article{DBLP:journals/corr/abs-1812-01097,
  author       = {Sebastian Caldas and
                  Peter Wu and
                  Tian Li and
                  Jakub Kone{\v{c}}n{\'y} and
                  H. Brendan McMahan and
                  Virginia Smith and
                  Ameet Talwalkar},
  title        = {{LEAF:} {A} Benchmark for Federated Settings},
  journal      = {CoRR},
  volume       = {abs/1812.01097},
  year         = {2018},
  url          = {http://arxiv.org/abs/1812.01097},
  eprinttype    = {arXiv},
  eprint       = {1812.01097},
  timestamp    = {Wed, 23 Dec 2020 09:35:18 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1812-01097.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2007-14390,
  author       = {Daniel J. Beutel and
                  Taner Topal and
                  Akhil Mathur and
                  Xinchi Qiu and
                  Titouan Parcollet and
                  Nicholas D. Lane},
  title        = {Flower: {A} Friendly Federated Learning Research Framework},
  journal      = {CoRR},
  volume       = {abs/2007.14390},
  year         = {2020},
  url          = {https://arxiv.org/abs/2007.14390},
  eprinttype    = {arXiv},
  eprint       = {2007.14390},
  timestamp    = {Mon, 03 Aug 2020 14:32:13 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2007-14390.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

Dataset Card Contact

In case of any doubts, please contact Flower Labs.