mlsanityData to 7 Oct 2026

FedLab: A Flexible Federated Learning Framework

arXiv 2107.11621 ↗First posted 24 Jul 2021cs.LG cs.AIcited by 54

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Abstract (arXiv)

Federated learning (FL) is a machine learning field in which researchers try to facilitate model learning process among multiparty without violating privacy protection regulations. Considerable effort has been invested in FL optimization and communication related researches. In this work, we introduce textttFedLab, a lightweight open-source framework for FL simulation. The design of textttFedLab focuses on FL algorithm effectiveness and communication efficiency. Also, textttFedLab is scalable in different deployment scenario. We hope textttFedLab could provide flexible API as well as reliable baseline implementations, and relieve the burden of implementing novel approaches for researchers in FL community.

Key facts. Cited by 54 papers in the map (data through 7 Oct 2026). Builds on 39 of its 55 references that are in the map. First posted on arXiv 24 Jul 2021.

Builds on

39 of 55 references.

References · 39

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  6. 2019Deep Leakage from Gradientscited by 1,004
  7. 2018LEAF: A Benchmark for Federated Settingscited by 780
  8. 2018How To Backdoor Federated Learningcited by 745
  9. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  10. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  11. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  12. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
27 more
  1. 2020Federated Learning with Matched Averagingcited by 564
  2. 2018signSGD: Compressed Optimisation for Non-Convex Problemscited by 561
  3. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  4. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  5. 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 471
  6. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  7. 2019Fair Resource Allocation in Federated Learningcited by 389
  8. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  9. 2018Sparsified SGD with Memorycited by 373
  10. 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 326
  11. 2019The Non-IID Data Quagmire of Decentralized Machine Learningcited by 303
  12. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  13. 2019Asynchronous Federated Optimizationcited by 264
  14. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 264
  15. 2017Sparse Communication for Distributed Gradient Descentcited by 243
  16. 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 219
  17. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  18. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 196
  19. 2015MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systemscited by 195
  20. 2021FedBABU: Towards Enhanced Representation for Federated Image Classificationcited by 180
  21. 2018A generic framework for privacy preserving deep learningcited by 103
  22. 2021Fast Federated Learning in the Presence of Arbitrary Device Unavailabilitycited by 72
  23. 2021Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modelingcited by 69
  24. 2020Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applicationscited by 58
  25. 20158-Bit Approximations for Parallelism in Deep Learningcited by 56
  26. 2019Understanding Top-k Sparsification in Distributed Deep Learningcited by 56
  27. 2020Federated Knowledge Distillationcited by 51

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 18; 2024: 18; 2025: 12; 2026: 2.

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  1. 2022When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning Methodscited by 74
  2. 2022FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Datacited by 49
  3. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
  4. 2023A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacycited by 24
  5. 2024Advances in APPFL: A Comprehensive and Extensible Federated Learning Frameworkcited by 19
  6. 2023BAFFLE: A Baseline of Backpropagation-Free Federated Learningcited by 12
  7. 2024Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directionscited by 12
  8. 2023FedNoisy: Federated Noisy Label Learning Benchmarkcited by 11
  9. 2023Privacy-Preserving Graph Machine Learning from Data to Computation: A Surveycited by 10
  10. 2024A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directionscited by 10
  11. 2022Completely Heterogeneous Federated Learningcited by 9
  12. 2024Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resiliencecited by 8
38 more
  1. 2024COALA: A Practical and Vision-Centric Federated Learning Platformcited by 7
  2. 2025HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmarkcited by 7
  3. 2025FNBench: Benchmarking Robust Federated Learning against Noisy Labelscited by 6
  4. 2023Federated Knowledge Graph Completion via Latent Embedding Sharing and Tensor Factorizationcited by 5
  5. 2024FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Modelscited by 5
  6. 2025A Thorough Assessment of the Non-IID Data Impact in Federated Learningcited by 5
  7. 2023StoCFL: A Stochastically Clustered Federated Learning Framework for Non-IID Data with Dynamic Client Participationcited by 4
  8. 2024FLEX: FLEXible Federated Learning Frameworkcited by 4
  9. 2023Federated Learning for Metaverse: A Surveycited by 3
  10. 2023Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectivescited by 3
  11. 2023FLASH-RL: Federated Learning Addressing System and Static Heterogeneity using Reinforcement Learningcited by 2
  12. 2024FedCVD: The First Real-World Federated Learning Benchmark on Cardiovascular Disease Datacited by 2
  13. 2023Towards Understanding Adversarial Transferability in Federated Learningcited by 1
  14. 2023Hire When You Need to: Gradual Participant Recruitment for Auction-based Federated Learningcited by 1
  15. 2023Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variancecited by 1
  16. 2023PAGE: Equilibrate Personalization and Generalization in Federated Learningcited by 1
  17. 2024The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustnesscited by 1
  18. 2024Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalizationcited by 1
  19. 2024Cohort Squeeze: Beyond a Single Communication Round per Cohort in Cross-Device Federated Learningcited by 1
  20. 2024Jigsaw Game: Federated Clusteringcited by 1
  21. 2025Integrating Identity-Based Identification against Adaptive Adversaries in Federated Learningcited by 1
  22. 2025PSI-PFL: Population Stability Index for Client Selection in non-IID Personalized Federated Learningcited by 1
  23. 2022Encoded Gradients Aggregation against Gradient Leakage in Federated Learningcited by 0
  24. 2023FedDCT: A Dynamic Cross-Tier Federated Learning Framework in Wireless Networkscited by 0
  25. 2023FedCME: Client Matching and Classifier Exchanging to Handle Data Heterogeneity in Federated Learningcited by 0
  26. 2023A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworkscited by 0
  27. 2023DCFL: Non-IID awareness Data Condensation aided Federated Learningcited by 0
  28. 2024FedDriveScore: Federated Scoring Driving Behavior with a Mixture of Metric Distributionscited by 0
  29. 2024Loop Improvement: An Efficient Approach for Extracting Shared Features from Heterogeneous Data without Central Servercited by 0
  30. 2024Fed-Credit: Robust Federated Learning with Credibility Managementcited by 0
  31. 2024Sparse-ProxSkip: Accelerated Sparse-to-Sparse Training in Federated Learningcited by 0
  32. 2024Where is the Testbed for my Federated Learning Research?cited by 0
  33. 2024Evaluating the Potential of Federated Learning for Maize Leaf Disease Predictioncited by 0
  34. 2025MedSegNet10: A Publicly Accessible Network Repository for Split Federated Medical Image Segmentationcited by 0
  35. 2025Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learningcited by 0
  36. 2025Federated Learning for ICD Classification with Lightweight Models and Pretrained Embeddingscited by 0
  37. 2025FLsim: A Modular and Library-Agnostic Simulation Framework for Federated Learningcited by 0
  38. 2025FLAMMABLE: A Multi-Model Federated Learning Framework with Multi-Model Engagement and Adaptive Batch Sizescited by 0

50 most cited of 54 All 54 (JSON)