FedMosaic
Collection
Benchmark proposed (and used) in FedMosaic (ICLR 2026) • 2 items • Updated
Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 81, in _split_generators
first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
fs: fsspec.AbstractFileSystem = fsspec.filesystem("memory")
~~~~~~~~~~~~~~~~~^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 302, in filesystem
cls = get_filesystem_class(protocol)
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 239, in get_filesystem_class
raise ValueError(f"Protocol not known: {protocol}")
ValueError: Protocol not known: memory
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 66, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
HFLB (Heterogeneous Federated Learning Benchmark)
FL Benchmark originally proposed in FedDAT, and modified by ourselves, splitting each dataset into different subtasks for task incremental learning setup in FedMosaic (ICLR 2026). Please checkout configuration of HFLB in the paper
Constituent Datasets
| Dataset | Task Type | Reference |
|---|---|---|
| GQA | Compositional visual reasoning | Hudson & Manning, CVPR 2019 |
| Abstract VQA | Abstract-scene visual question answering | Antol et al., ICCV 2015 |
| SNLI-VE | Visual entailment | Xie et al., arXiv 2019 |
| COCO-QA | Image question answering | Ren et al., NeurIPS 2015 |
| NLVR2 | Natural-language visual reasoning over image pairs | Suhr et al., ACL 2019 |
| VizWiz | Accessibility-focused VQA | Gurari et al., CVPR 2018 |
| NLVR2 | Dual-image visual reasoning | Suhr et al., ACL 2019 |
| AQUA | Art-domain visual question answering | Garcia et al., ECCV Workshops 2020 |
How to Download
We highly recommend downloading each dataset (.tar) file separately:
# Example: Download GQA
huggingface-cli download SNUMPR/HFLB GQA.tar --local-dir ./ --repo-type dataset
# Example: Download AQUA
huggingface-cli download SNUMPR/HFLB AQUA.tar --local-dir ./ --repo-type dataset
After downloading, extract each archive:
tar -xvf AQUA.tar
# Repeat for other archives
Place extracted data under the dataset/ folder in the code repository, following the structure described in the README.
Dataset Credits & References
HFLB builds on the following publicly available datasets.
@inproceedings{hudson2019gqa,
title = {GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering},
author = {Hudson, Drew A. and Manning, Christopher D.},
booktitle = {CVPR},
year = {2019}
}
@inproceedings{antol2015vqa,
title = {VQA: Visual Question Answering},
author = {Antol, Stanislaw and Agrawal, Aishwarya and Lu, Jiasen and Mitchell, Margaret and Batra, Dhruv and Zitnick, C. Lawrence and Parikh, Devi},
booktitle = {ICCV},
year = {2015}
}
@article{xie2019snlive,
title = {Visual Entailment: A Novel Task for Fine-Grained Image Understanding},
author = {Xie, Ning and Lai, Farley and Doran, Derek and Kadav, Asim},
journal = {arXiv preprint arXiv:1901.06706},
year = {2019}
}
@inproceedings{ren2015cocoqa,
title = {Exploring Models and Data for Image Question Answering},
author = {Ren, Mengye and Kiros, Ryan and Zemel, Richard S.},
booktitle = {NeurIPS},
year = {2015}
}
@inproceedings{suhr2019nlvr2,
title = {A Corpus for Reasoning about Natural Language Grounded in Photographs},
author = {Suhr, Alane and Zhou, Stephanie and Zhang, Ally and Zhang, Iris and Bai, Huajun and Artzi, Yoav},
booktitle = {ACL},
year = {2019}
}
@inproceedings{gurari2018vizwiz,
title = {VizWiz Grand Challenge: Answering Visual Questions from Blind People},
author = {Gurari, Danna and Li, Qing and Stangl, Abigale J. and Guo, Anhong and Lin, Chi and Grauman, Kristen and Luo, Jiebo and Bigham, Jeffrey P.},
booktitle = {CVPR},
year = {2018}
}
@inproceedings{garcia2020aqua,
title = {A Dataset and Baselines for Visual Question Answering on Art},
author = {Garcia, Noa and Ye, Chentao and Liu, Zihua and Hu, Qingtao and Otani, Mayu and Chu, Chenhui and Nakashima, Yuta and Mitamura, Teruko},
booktitle = {ECCV Workshops},
year = {2020}
}
Citation
If you use HFLB in your research, please cite FedDAT paper and our paper:
@inproceedings{chen2023feddat,
title={FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning},
author={Chen, Haokun and Zhang, Yao and Krompass, Denis and Gu, Jindong and Tresp, Volker},
booktitle={AAAI},
year={2024}
}
@inproceedings{seo2026colora,
title = {Co-LoRA: Collaborative Model Personalization on Heterogeneous Multi-Modal Clients},
author = {Seo, Minhyuk and Kim, Taeheon and Lee, Hankook and Choi, Jonghyun and Tuytelaars, Tinne},
booktitle = {The Fourteenth International Conference on Learning Representations (ICLR)},
year = {2026},
url = {https://openreview.net/forum?id=0g5Dk4Qfh0}
}
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