FLAIR is a large labelled image dataset designed for benchmarking in federated learning. It was published at NeurIPS 2022 by Apple and captures characteristics encountered in federated learning scenarios. The dataset was last updated on the Hugging Face platform on 2024-05-27.
Use Cases
- Benchmarking federated learning algorithms based on the dataset's labelled images.
- Developing new federated learning methods based on the characteristics described in the dataset.
- Exploring data heterogeneity and client simulation in federated learning based on the dataset's design.
Strengths
- Dataset was published at the NeurIPS 2022 conference.
- Dataset is designed to capture characteristics specific to federated learning.
- Dataset was last updated on 2024-05-27.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- Description metadata is limited; actual data quality requires manual inspection after download.
Provenance
- Source
- Apple
- Freshness
- Last updated 2024-05-27 21:22:51.