mlsanityData to 7 Oct 2026

OpenFL: An open-source framework for Federated Learning

arXiv 2105.06413 ↗First posted 13 May 2021cs.LG cs.DCcited by 58

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Abstract (arXiv)

Federated learning (FL) is a computational paradigm that enables organizations to collaborate on machine learning (ML) projects without sharing sensitive data, such as, patient records, financial data, or classified secrets. Open Federated Learning (OpenFL https://github.com/intel/openfl) is an open-source framework for training ML algorithms using the data-private collaborative learning paradigm of FL. OpenFL works with training pipelines built with both TensorFlow and PyTorch, and can be easily extended to other ML and deep learning frameworks. Here, we summarize the motivation and development characteristics of OpenFL, with the intention of facilitating its application to existing ML model training in a production environment. Finally, we describe the first use of the OpenFL framework to train consensus ML models in a consortium of international healthcare organizations, as well as how it facilitates the first computational competition on FL.

Key facts. Cited by 58 papers in the map (data through 7 Oct 2026). Builds on 18 of its 46 references that are in the map. First posted on arXiv 13 May 2021.

Builds on

18 of 46 references.

References · 18

  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. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  4. 2019Advances and Open Problems in Federated Learningcited by 3,021
  5. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,201
  6. 2019Towards Federated Learning at Scale: System Designcited by 826
  7. 2021SUPERB: Speech processing Universal PERformance Benchmarkcited by 652
  8. 2020The Future of Digital Health with Federated Learningcited by 625
  9. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 508
  10. 2018Confounding variables can degrade generalization performance of radiological deep learning modelscited by 374
  11. 2020Data and its (dis)contents: A survey of dataset development and use in machine learning researchcited by 259
  12. 2020Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentationcited by 212
6 more
  1. 2015MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systemscited by 195
  2. 2018Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentationcited by 148
  3. 2021The Federated Tumor Segmentation (FeTS) Challengecited by 49
  4. 2019The reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort studycited by 38
  5. 2019Towards Federated Graph Learning for Collaborative Financial Crimes Detectioncited by 33
  6. 2021GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imagingcited by 25

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 7; 2023: 12; 2024: 19; 2025: 9; 2026: 8.

58 papers.

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  1. 2021MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluationcited by 88
  2. 2022Federated Learning Enables Big Data for Rare Cancer Boundary Detectioncited by 83
  3. 2022NVIDIA FLARE: Federated Learning from Simulation to Real-Worldcited by 78
  4. 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
  5. 2024A Survey on Efficient Federated Learning Methods for Foundation Model Trainingcited by 29
  6. 2022CrowdGuard: Federated Backdoor Detection in Federated Learningcited by 26
  7. 2023FS-Real: Towards Real-World Cross-Device Federated Learningcited by 22
  8. 2024Advances in APPFL: A Comprehensive and Extensible Federated Learning Frameworkcited by 19
  9. 2024From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcarecited by 16
  10. 2024Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directionscited by 12
  11. 2021FedCostWAvg: A new averaging for better Federated Learningcited by 11
  12. 2023Recent Methodological Advances in Federated Learning for Healthcarecited by 11
38 more
  1. 2024Training Machine Learning models at the Edge: A Surveycited by 11
  2. 2022Enhancing Heterogeneous Federated Learning with Knowledge Extraction and Multi-Model Fusioncited by 10
  3. 2023Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applicationscited by 10
  4. 2024Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimationcited by 9
  5. 2025Atlas: A Framework for ML Lifecycle Provenance & Transparencycited by 8
  6. 2023FedPIDAvg: A PID controller inspired aggregation method for Federated Learningcited by 7
  7. 2023Flame: Simplifying Topology Extension in Federated Learningcited by 7
  8. 2024FLEX: FLEXible Federated Learning Frameworkcited by 4
  9. 2021SecFL: Confidential Federated Learning using TEEscited by 3
  10. 2023Experimenting with Emerging RISC-V Systems for Decentralised Machine Learningcited by 3
  11. 2023Model-Agnostic Federated Learningcited by 3
  12. 2023Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectivescited by 3
  13. 2024Federated Linear Contextual Bandits with Heterogeneous Clientscited by 3
  14. 2024Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systemscited by 3
  15. 2024Federated and Transfer Learning for Cancer Detection Based on Image Analysiscited by 3
  16. 2024Flight: A FaaS-Based Framework for Complex and Hierarchical Federated Learningcited by 3
  17. 2026Federated Modality-specific Encoders and Partially Personalized Fusion Decoder for Multimodal Brain Tumor Segmentationcited by 3
  18. 2023Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Modelscited by 2
  19. 2024Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Reviewcited by 2
  20. 2025Technical and Legal Aspects of Federated Learning in Bioinformatics: Applications, Challenges and Opportunitiescited by 2
  21. 2025On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directionscited by 2
  22. 2025The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learningcited by 2
  23. 2022Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor Segmentationcited by 1
  24. 2022MammoFL: Mammographic Breast Density Estimation using Federated Learningcited by 1
  25. 2024Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Reviewcited by 1
  26. 2025Federated prediction for scalable and privacy-preserved knowledge-based planning in radiotherapycited by 1
  27. 2025OmniFed: A Modular Framework for Configurable Federated Learning from Edge to HPCcited by 1
  28. 2026Benchmarking Federated Learning in Edge Computing Environments: A Systematic Review and Performance Evaluationcited by 1
  29. 2026Scalable Cross-Facility Federated Learning for Scientific Foundation Models on Multiple Supercomputerscited by 1
  30. 2026Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysiscited by 1
  31. 2022Decision Models for Selecting Federated Learning Architecture Patternscited by 0
  32. 2023A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworkscited by 0
  33. 2023The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learningcited by 0
  34. 2024Exploring Federated Deep Learning for Standardising Naming Conventions in Radiotherapy Datacited by 0
  35. 2024Privacy-Enhancing Collaborative Information Sharing through Federated Learning -- A Case of the Insurance Industrycited by 0
  36. 2024Federated Computing -- Survey on Building Blocks, Extensions and Systemscited by 0
  37. 2024Where is the Testbed for my Federated Learning Research?cited by 0
  38. 2024Stalactite: Toolbox for Fast Prototyping of Vertical Federated Learning Systemscited by 0

50 most cited of 58 All 58 (JSON)