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End of preview. Expand
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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.
- Install the package.
pip install flwr-datasets
- 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.
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