mlsanityData to 7 Oct 2026

NVIDIA FLARE: Federated Learning from Simulation to Real-World

arXiv 2210.13291 ↗First posted 24 Oct 2022cs.LG cs.AI cs.CVcited by 78

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Abstract (arXiv)

Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without centralizing the data. We created NVIDIA FLARE as an open-source software development kit (SDK) to make it easier for data scientists to use FL in their research and real-world applications. The SDK includes solutions for state-of-the-art FL algorithms and federated machine learning approaches, which facilitate building workflows for distributed learning across enterprises and enable platform developers to create a secure, privacy-preserving offering for multiparty collaboration utilizing homomorphic encryption or differential privacy. The SDK is a lightweight, flexible, and scalable Python package. It allows researchers to apply their data science workflows in any training libraries (PyTorch, TensorFlow, XGBoost, or even NumPy) in real-world FL settings. This paper introduces the key design principles of NVFlare and illustrates some use cases (e.g., COVID analysis) with customizable FL workflows that implement different privacy-preserving algorithms. Code is available at https://github.com/NVIDIA/NVFlare.

Key facts. Cited by 78 papers in the map (data through 7 Oct 2026). Builds on 29 of its 51 references that are in the map. First posted on arXiv 24 Oct 2022.

Builds on

29 of 51 references.

References · 29

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  4. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  5. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,195
  6. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  7. 2019Advances and Open Problems in Federated Learningcited by 3,021
  8. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  9. 2016Deep Learning with Differential Privacycited by 2,682
  10. 2020Adaptive Federated Optimizationcited by 1,036
  11. 2021Towards a Unified View of Parameter-Efficient Transfer Learningcited by 863
  12. 2021GPT Understands, Toocited by 715
17 more
  1. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  2. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  3. 2020The Future of Digital Health with Federated Learningcited by 625
  4. 2020Federated Learning with Matched Averagingcited by 564
  5. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  6. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  7. 2018Distributed learning of deep neural network over multiple agentscited by 277
  8. 2020FetchSGD: Communication-Efficient Federated Learning with Sketchingcited by 178
  9. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
  10. 2018Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentationcited by 148
  11. 2020Federated Learning for Breast Density Classification: A Real-World Implementationcited by 62
  12. 2021OpenFL: An open-source framework for Federated Learningcited by 58
  13. 2020IBM Federated Learning: an Enterprise Framework White Paper V0.1cited by 57
  14. 2022Do Gradient Inversion Attacks Make Federated Learning Unsafe?cited by 44
  15. 2022Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentationcited by 41
  16. 2022FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulationscited by 27
  17. 2022Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentationcited by 16

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Citing papers per year: 2022: 1; 2023: 17; 2024: 26; 2025: 18; 2026: 16.

78 papers.

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  1. 2023FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning Systemcited by 38
  2. 2024A Survey on Efficient Federated Learning Methods for Foundation Model Trainingcited by 29
  3. 2023FS-Real: Towards Real-World Cross-Device Federated Learningcited by 22
  4. 2023FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMscited by 19
  5. 2024Advances in APPFL: A Comprehensive and Extensible Federated Learning Frameworkcited by 19
  6. 2023Decentralized Learning Made Easy with DecentralizePycited by 18
  7. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
  8. 2024From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcarecited by 16
  9. 2024The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscapecited by 13
  10. 2023Recent Methodological Advances in Federated Learning for Healthcarecited by 11
  11. 2023Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applicationscited by 10
  12. 2024Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimationcited by 9
38 more
  1. 2024pfl-research: simulation framework for accelerating research in Private Federated Learningcited by 8
  2. 2023Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMIcited by 6
  3. 2023When Federated Learning meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protectioncited by 6
  4. 2023ConDistFL: Conditional Distillation for Federated Learning from Partially Annotated Datacited by 6
  5. 2025Byzantine-Robust Federated Learning over Ring-All-Reduce Distributed Computingcited by 6
  6. 2023APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Servicecited by 4
  7. 2024Empowering Federated Learning for Massive Models with NVIDIA FLAREcited by 4
  8. 2024Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspectivecited by 4
  9. 2024Enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applicationscited by 4
  10. 2023Model-Agnostic Federated Learningcited by 3
  11. 2023Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectivescited by 3
  12. 2024A Federated Learning Benchmark on Tabular Data: Comparing Tree-Based Models and Neural Networkscited by 3
  13. 2024Overcoming the Challenges of Batch Normalization in Federated Learningcited by 3
  14. 2024Unity is Power: Semi-Asynchronous Collaborative Training of Large-Scale Models with Structured Pruning in Resource-Limited Clientscited by 3
  15. 2024Review of Mathematical Optimization in Federated Learningcited by 3
  16. 2025FL-APU: A Software Architecture to Ease Practical Implementation of Cross-Silo Federated Learningcited by 3
  17. 2025Don't Reach for the Stars: Rethinking Topology for Resilient Federated Learningcited by 3
  18. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  19. 2023Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Modelscited by 2
  20. 2023Project Florida: Federated Learning Made Easycited by 2
  21. 2024On the Volatility of Shapley-Based Contribution Metrics in Federated Learningcited by 2
  22. 2024Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Reviewcited by 2
  23. 2025On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directionscited by 2
  24. 2025The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learningcited by 2
  25. 2023ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual Classificationcited by 1
  26. 2024Histogram-Based Federated XGBoost using Minimal Variance Sampling for Federated Tabular Datacited by 1
  27. 2025A Federated Random Forest Solution for Secure Distributed Machine Learningcited by 1
  28. 2025Federated prediction for scalable and privacy-preserved knowledge-based planning in radiotherapycited by 1
  29. 2025From Connectivity to Autonomy: The Dawn of Self-Evolving Communication Systemscited by 1
  30. 2025Federated Fine-tuning of SAM-Med3D for MRI-based Dementia Classificationcited by 1
  31. 2025OmniFed: A Modular Framework for Configurable Federated Learning from Edge to HPCcited by 1
  32. 2025Helmsman: Autonomous Synthesis of Federated Learning Systems via Collaborative LLM Agentscited by 1
  33. 2025DictPFL: Efficient and Private Federated Learning on Encrypted Gradientscited by 1
  34. 2026Scalable Cross-Facility Federated Learning for Scientific Foundation Models on Multiple Supercomputerscited by 1
  35. 2023A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworkscited by 0
  36. 2023MetisFL: An Embarrassingly Parallelized Controller for Scalable & Efficient Federated Learning Workflowscited by 0
  37. 2024Leveraging Federated Learning for Automatic Detection of Clopidogrel Treatment Failurescited by 0
  38. 2024Where is the Testbed for my Federated Learning Research?cited by 0

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