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LEAF: A Benchmark for Federated Settings

arXiv 1812.01097 ↗First posted 3 Dec 2018cs.LG stat.MLcited by 780

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Abstract (arXiv)

Modern federated networks, such as those comprised of wearable devices, mobile phones, or autonomous vehicles, generate massive amounts of data each day. This wealth of data can help to learn models that can improve the user experience on each device. However, the scale and heterogeneity of federated data presents new challenges in research areas such as federated learning, meta-learning, and multi-task learning. As the machine learning community begins to tackle these challenges, we are at a critical time to ensure that developments made in these areas are grounded with realistic benchmarks. To this end, we propose LEAF, a modular benchmarking framework for learning in federated settings. LEAF includes a suite of open-source federated datasets, a rigorous evaluation framework, and a set of reference implementations, all geared towards capturing the obstacles and intricacies of practical federated environments.

Key facts. Cited by 780 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Dec 2018.

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Citing papers per year: 2020: 1; 2021: 144; 2022: 171; 2023: 166; 2024: 125; 2025: 111; 2026: 62.

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  1. 2021Model-Contrastive Federated Learningcited by 762
  2. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  3. 2021Federated Learning Based on Dynamic Regularizationcited by 555
  4. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 537
  5. 2021Towards Personalized Federated Learningcited by 462
  6. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 380
  7. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 349
  8. 2021A Field Guide to Federated Optimizationcited by 312
  9. 2021FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropoutcited by 192
  10. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 189
  11. 2021Federated Learning with Buffered Asynchronous Aggregationcited by 188
  12. 2023Towards Building the Federated GPT: Federated Instruction Tuningcited by 184
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  1. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  2. 2023FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learningcited by 146
  3. 2021FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learningcited by 140
  4. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 137
  5. 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 135
  6. 2021The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregationcited by 127
  7. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 121
  8. 2022FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clientscited by 119
  9. 2021Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learningcited by 116
  10. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 112
  11. 2021Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learningcited by 111
  12. 2022The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retrainingcited by 109
  13. 2021FedMix: Approximation of Mixup under Mean Augmented Federated Learningcited by 108
  14. 2022Federated Learning with Label Distribution Skew via Logits Calibrationcited by 108
  15. 2021Distributionally Robust Federated Averagingcited by 104
  16. 2021Trustworthy AI: A Computational Perspectivecited by 93
  17. 2021Papaya: Practical, Private, and Scalable Federated Learningcited by 89
  18. 2021Heterogeneity for the Win: One-Shot Federated Clusteringcited by 87
  19. 2021FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Taskscited by 84
  20. 2021Continual Learning of a Mixed Sequence of Similar and Dissimilar Taskscited by 80
  21. 2021On Large-Cohort Training for Federated Learningcited by 73
  22. 2022Divergence-aware Federated Self-Supervised Learningcited by 71
  23. 2022On the Importance and Applicability of Pre-Training for Federated Learningcited by 71
  24. 2022Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learningcited by 71
  25. 2023FedExP: Speeding Up Federated Averaging via Extrapolationcited by 68
  26. 2021Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacycited by 66
  27. 2021The Skellam Mechanism for Differentially Private Federated Learningcited by 66
  28. 2023FedDisco: Federated Learning with Discrepancy-Aware Collaborationcited by 66
  29. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
  30. 2022FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learningcited by 65
  31. 2021LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learningcited by 63
  32. 2021What Do We Mean by Generalization in Federated Learning?cited by 62
  33. 2022FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learningcited by 62
  34. 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
  35. 2021Federated Learning with Fair Averagingcited by 59
  36. 2021Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharingcited by 58
  37. 2021Privacy-Preserving Machine Learning: Methods, Challenges and Directionscited by 58
  38. 2021Personalized Federated Learning through Local Memorizationcited by 57

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