| | --- |
| | task_categories: |
| | - question-answering |
| | language: |
| | - en |
| | tags: |
| | - agent |
| | size_categories: |
| | - 100K<n<1M |
| | --- |
| | |
| | # HFLB (Heterogeneous Federated Learning Benchmark) |
| |
|
| | FL Benchmark originally proposed in [FedDAT](https://arxiv.org/abs/2308.12305), and modified by ourselves, splitting each dataset into different subtasks for task incremental learning setup in [FedMosaic (ICLR 2026)](https://openreview.net/forum?id=0g5Dk4Qfh0). |
| | Please checkout configuration of HFLB in the [paper](https://openreview.net/forum?id=0g5Dk4Qfh0) |
| |
|
| | ### 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: |
| |
|
| | ```bash |
| | # 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: |
| | ```bash |
| | tar -xvf AQUA.tar |
| | # Repeat for other archives |
| | ``` |
| |
|
| | Place extracted data under the `dataset/` folder in the [code repository](https://github.com/snumprlab/fedmosaic), following the structure described in the [README](https://github.com/snumprlab/fedmosaic/blob/main/README.md). |
| |
|
| | --- |
| |
|
| | <details> |
| | <summary>Dataset Credits & References</summary> |
| |
|
| | HFLB builds on the following publicly available datasets. |
| |
|
| | ```bibtex |
| | @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} |
| | } |
| | ``` |
| | </details> |
| | --- |
| |
|
| | ## Citation |
| |
|
| | If you use HFLB in your research, please cite FedDAT paper and our paper: |
| |
|
| | ```bibtex |
| | @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} |
| | } |
| | ``` |
