# FLAIR: Federated Learning Annotated Image Repository

arXiv: https://arxiv.org/abs/2207.08869 · HTML: https://mlsanity.com/paper/2207.08869/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 76 references that are in the map.
- First posted on arXiv 18 Jul 2022.

## Builds on (most cited first, 30 of 38)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Do ImageNet Classifiers Generalize to ImageNet?](https://mlsanity.com/paper/1902.10811/) (2019)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Exploiting Unintended Feature Leakage in Collaborative Learning](https://mlsanity.com/paper/1805.04049/) (2018)
- [Adaptive Personalized Federated Learning](https://mlsanity.com/paper/2003.13461/) (2020)
- [Differential Privacy Has Disparate Impact on Model Accuracy](https://mlsanity.com/paper/1905.12101/) (2019)
- [Personalized Federated Learning: A Meta-Learning Approach](https://mlsanity.com/paper/2002.07948/) (2020)
- [FedML: A Research Library and Benchmark for Federated Machine Learning](https://mlsanity.com/paper/2007.13518/) (2020)
- [Differentially Private Learning with Adaptive Clipping](https://mlsanity.com/paper/1905.03871/) (2019)
- [Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning](https://mlsanity.com/paper/2008.03606/) (2020)
- [Salvaging Federated Learning by Local Adaptation](https://mlsanity.com/paper/2002.04758/) (2020)
- [Federated Visual Classification with Real-World Data Distribution](https://mlsanity.com/paper/2003.08082/) (2020)

## Built on by (most cited first, 22 of 22)

- [FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models](https://mlsanity.com/paper/2406.04845/) (2024)
- [Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning](https://mlsanity.com/paper/2307.09619/) (2023)
- [Federated LoRA with Sparse Communication](https://mlsanity.com/paper/2406.05233/) (2024)
- [On the Efficacy of Differentially Private Few-shot Image Classification](https://mlsanity.com/paper/2302.01190/) (2023)
- [FedNoisy: Federated Noisy Label Learning Benchmark](https://mlsanity.com/paper/2306.11650/) (2023)
- [DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation](https://mlsanity.com/paper/2405.06368/) (2024)
- [pfl-research: simulation framework for accelerating research in Private Federated Learning](https://mlsanity.com/paper/2404.06430/) (2024)
- [Fake It Till Make It: Federated Learning with Consensus-Oriented Generation](https://mlsanity.com/paper/2312.05966/) (2023)
- [Mitigating Group Bias in Federated Learning for Heterogeneous Devices](https://mlsanity.com/paper/2309.07085/) (2023)
- [Federated Fairness Analytics: Quantifying Fairness in Federated Learning](https://mlsanity.com/paper/2408.08214/) (2024)
- [Momentum Approximation in Asynchronous Private Federated Learning](https://mlsanity.com/paper/2402.09247/) (2024)
- [Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials](https://mlsanity.com/paper/2406.02416/) (2024)
- [FedRS-Bench: Realistic Federated Learning Datasets and Benchmarks in Remote Sensing](https://mlsanity.com/paper/2505.08325/) (2025)
- [FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units](https://mlsanity.com/paper/2401.12862/) (2024)
- [Optimizing Personalized Federated Learning through Adaptive Layer-Wise Learning](https://mlsanity.com/paper/2412.07062/) (2024)
- [HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2403.04207/) (2024)
- [Federated Learning with Only Positive Labels by Exploring Label Correlations](https://mlsanity.com/paper/2404.15598/) (2024)
- [FedHarmony: Harmonizing Heterogeneous Label Correlations in Federated Multi-Label Learning](https://mlsanity.com/paper/2604.28024/) (2026)
- [Cross-client Label Propagation for Transductive and Semi-Supervised Federated Learning](https://mlsanity.com/paper/2210.06434/) (2022)
- [Where is the Testbed for my Federated Learning Research?](https://mlsanity.com/paper/2407.14154/) (2024)
- [FLIP: Towards Comprehensive and Reliable Evaluation of Federated Prompt Learning](https://mlsanity.com/paper/2503.22263/) (2025)
- [Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation](https://mlsanity.com/paper/2604.20596/) (2026)

## Abstract (arXiv)

Cross-device federated learning is an emerging machine learning (ML) paradigm where a large population of devices collectively train an ML model while the data remains on the devices. This research field has a unique set of practical challenges, and to systematically make advances, new datasets curated to be compatible with this paradigm are needed. Existing federated learning benchmarks in the image domain do not accurately capture the scale and heterogeneity of many real-world use cases. We introduce FLAIR, a challenging large-scale annotated image dataset for multi-label classification suitable for federated learning. FLAIR has 429,078 images from 51,414 Flickr users and captures many of the intricacies typically encountered in federated learning, such as heterogeneous user data and a long-tailed label distribution. We implement multiple baselines in different learning setups for different tasks on this dataset. We believe FLAIR can serve as a challenging benchmark for advancing the state-of-the art in federated learning. Dataset access and the code for the benchmark are available at urlhttps://github.com/apple/ml-flair.