mlsanityData to 14 Oct 2026

FLAIR: Federated Learning Annotated Image Repository

arXiv 2207.08869 ↗First posted 18 Jul 2022cs.LG cs.CR cs.CVcited by 22

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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.

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.

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38 of 76 references.

References · 38

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  5. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  6. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  7. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  8. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  9. 2014Deep Learning Face Attributes in the Wildcited by 3,693
  10. 2019Advances and Open Problems in Federated Learningcited by 3,046
  11. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  12. 2016Deep Learning with Differential Privacycited by 2,728
26 more
  1. 2016Matching Networks for One Shot Learningcited by 2,255
  2. 2018Group Normalizationcited by 1,524
  3. 2019Do ImageNet Classifiers Generalize to ImageNet?cited by 1,515
  4. 2020Adaptive Federated Optimizationcited by 1,046
  5. 2019Deep Leakage from Gradientscited by 1,021
  6. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  7. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  8. 2018LEAF: A Benchmark for Federated Settingscited by 792
  9. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
  10. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 520
  11. 2020Adaptive Personalized Federated Learningcited by 358
  12. 2019Differential Privacy Has Disparate Impact on Model Accuracycited by 316
  13. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 312
  14. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 300
  15. 2019Differentially Private Learning with Adaptive Clippingcited by 220
  16. 2020Mime: Mimicking Centralized Stochastic Algorithms in Federated Learningcited by 154
  17. 2020Salvaging Federated Learning by Local Adaptationcited by 142
  18. 2020Federated Visual Classification with Real-World Data Distributioncited by 128
  19. 2021FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Taskscited by 85
  20. 2021Public Data-Assisted Mirror Descent for Private Model Trainingcited by 49
  21. 2021Private Adaptive Gradient Methods for Convex Optimizationcited by 46
  22. 2022Private Adaptive Optimization with Side Informationcited by 38
  23. 2021FedCV: A Federated Learning Framework for Diverse Computer Vision Taskscited by 32
  24. 2020The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systemscited by 31
  25. 2020Improving on-device speaker verification using federated learning with privacycited by 30
  26. 2021FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networkscited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 5; 2024: 12; 2025: 2; 2026: 2.

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  1. 2024FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Modelscited by 30
  2. 2023Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learningcited by 29
  3. 2024Federated LoRA with Sparse Communicationcited by 26
  4. 2023On the Efficacy of Differentially Private Few-shot Image Classificationcited by 12
  5. 2023FedNoisy: Federated Noisy Label Learning Benchmarkcited by 11
  6. 2024DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptationcited by 9
  7. 2024pfl-research: simulation framework for accelerating research in Private Federated Learningcited by 8
  8. 2023Fake It Till Make It: Federated Learning with Consensus-Oriented Generationcited by 6
  9. 2023Mitigating Group Bias in Federated Learning for Heterogeneous Devicescited by 5
  10. 2024Federated Fairness Analytics: Quantifying Fairness in Federated Learningcited by 4
  11. 2024Momentum Approximation in Asynchronous Private Federated Learningcited by 3
  12. 2024Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomialscited by 3
10 more
  1. 2025FedRS-Bench: Realistic Federated Learning Datasets and Benchmarks in Remote Sensingcited by 3
  2. 2024FedRSU: Federated Learning for Scene Flow Estimation on Roadside Unitscited by 2
  3. 2024Optimizing Personalized Federated Learning through Adaptive Layer-Wise Learningcited by 2
  4. 2024HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learningcited by 1
  5. 2024Federated Learning with Only Positive Labels by Exploring Label Correlationscited by 1
  6. 2026FedHarmony: Harmonizing Heterogeneous Label Correlations in Federated Multi-Label Learningcited by 1
  7. 2022Cross-client Label Propagation for Transductive and Semi-Supervised Federated Learningcited by 0
  8. 2024Where is the Testbed for my Federated Learning Research?cited by 0
  9. 2025FLIP: Towards Comprehensive and Reliable Evaluation of Federated Prompt Learningcited by 0
  10. 2026Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregationcited by 0