| --- |
| license: cc-by-4.0 |
| task_categories: |
| - tabular-classification |
| language: |
| - en |
| tags: |
| - federated-learning |
| - medical |
| - chest-xray |
| - fairness |
| - healthcare |
| - benchmark |
| - chexpert |
| - densenet |
| pretty_name: FL-CheX Federated Learning Benchmark |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # FL-CheX: Federated Learning Benchmark for Chest Disease Classification |
|
|
| **Author:** Md. Sajjad Ullah |
| **Affiliation:** CSE, University of Asia Pacific, Bangladesh |
| **Date:** March 2026 |
|
|
| ## Dataset Summary |
| FL-CheX is the first federated learning benchmark |
| combining demographic, Non-IID, and scanner heterogeneity |
| for chest disease classification research. |
|
|
| 15 pre-partitioned FL client nodes built from CheXpert |
| (Stanford) with real DenseNet-121 features. |
|
|
| ## Load Dataset |
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("mdsajjadullah/fl-chex-benchmark") |
| ``` |
|
|
| ## Dataset Structure |
| - 5 Demographic nodes (age × gender) |
| - 5 Non-IID Hospital nodes (disease-specialized) |
| - 5 Scanner nodes (hardware quality variation) |
|
|
| ## Citation |
| ```bibtex |
| @misc{flchex2026, |
| author = {Md. Sajjad Ullah}, |
| title = {FL-CheX: A Federated Learning Benchmark}, |
| year = {2026} |
| } |
| ``` |
| ``` |
