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--- |
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license: bsd-2-clause |
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dataset_info: |
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features: |
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- name: device_id |
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dtype: string |
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- name: x |
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sequence: float64 |
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- name: 'y' |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 53656756 |
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num_examples: 107553 |
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download_size: 64693258 |
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dataset_size: 53656756 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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task_categories: |
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- tabular-classification |
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size_categories: |
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- 100K<n<1M |
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--- |
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# Dataset Card for SYNTHETIC |
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The SYNTHETIC dataset is a part of the [LEAF](https://leaf.cmu.edu/) benchmark. |
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This version corresponds to the dataset generated with default parameters that give a dataset with: |
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* input (`x`) of length 60; |
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* 5 unique labels (`y`) |
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* 1000 unique devices (`device_id`). |
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## Dataset Details |
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### Dataset Description |
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- **Curated by:** [LEAF](https://leaf.cmu.edu/) |
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- **License:** BSD 2-Clause License |
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## Uses |
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This dataset is intended to be used in Federated Learning settings. |
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### Direct Use |
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We recommend using [Flower Dataset](https://flower.ai/docs/datasets/) (flwr-datasets) and [Flower](https://flower.ai/docs/framework/) (flwr). |
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To partition the dataset, do the following. |
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1. Install the package. |
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```bash |
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pip install flwr-datasets |
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``` |
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2. Use the HF Dataset under the hood in Flower Datasets. |
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```python |
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from flwr_datasets import FederatedDataset |
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from flwr_datasets.partitioner import NaturalIdPartitioner |
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fds = FederatedDataset( |
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dataset="flwrlabs/synthetic", |
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partitioners={"train": NaturalIdPartitioner(partition_by="device_id")} |
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) |
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partition = fds.load_partition(partition_id=0) |
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``` |
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## Dataset Structure |
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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). |
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## Citation |
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When working on the LEAF benchmark, please cite the original paper. If you're using this dataset with Flower Datasets, you can cite Flower. |
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**BibTeX:** |
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``` |
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@article{DBLP:journals/corr/abs-1812-01097, |
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author = {Sebastian Caldas and |
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Peter Wu and |
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Tian Li and |
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Jakub Kone{\v{c}}n{\'y} and |
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H. Brendan McMahan and |
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Virginia Smith and |
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Ameet Talwalkar}, |
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title = {{LEAF:} {A} Benchmark for Federated Settings}, |
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journal = {CoRR}, |
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volume = {abs/1812.01097}, |
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year = {2018}, |
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url = {http://arxiv.org/abs/1812.01097}, |
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eprinttype = {arXiv}, |
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eprint = {1812.01097}, |
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timestamp = {Wed, 23 Dec 2020 09:35:18 +0100}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-1812-01097.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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``` |
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``` |
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@article{DBLP:journals/corr/abs-2007-14390, |
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author = {Daniel J. Beutel and |
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Taner Topal and |
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Akhil Mathur and |
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Xinchi Qiu and |
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Titouan Parcollet and |
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Nicholas D. Lane}, |
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title = {Flower: {A} Friendly Federated Learning Research Framework}, |
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journal = {CoRR}, |
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volume = {abs/2007.14390}, |
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year = {2020}, |
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url = {https://arxiv.org/abs/2007.14390}, |
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eprinttype = {arXiv}, |
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eprint = {2007.14390}, |
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timestamp = {Mon, 03 Aug 2020 14:32:13 +0200}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-2007-14390.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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``` |
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## Dataset Card Contact |
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In case of any doubts, please contact [Flower Labs](https://flower.ai/). |
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