# NVFlare

> NVIDIA Federated Learning Application Runtime Environment

**Wikidata**: [Q127474913](https://www.wikidata.org/wiki/Q127474913)  
**Source**: https://4ort.xyz/entity/nvflare

## Summary
NVFlare is the NVIDIA Federated Learning Application Runtime Environment, an open-source software framework designed to enable secure, distributed machine learning across decentralized data sources. It addresses privacy concerns by allowing AI models to be trained locally without centralizing sensitive data.

## Key Facts
- NVFlare is licensed under Apache Software License 2.0, permitting free use, modification, and redistribution.
- Stable versions span from 2.0.0 (November 23, 2021) to 2.0.16 (March 17, 2022), with rapid release cycles.
- The project is hosted at https://github.com/NVIDIA/NVFlare, with official documentation at https://nvidia.github.io/NVFlare.
- It is classified as both software and free software, emphasizing its open-source nature.
- Development aligns with the research paper "NVIDIA FLARE: Federated Learning from Simulation to Real-World".

## FAQs
### Q: What is NVFlare used for?
A: NVFlare enables federated learning workflows, allowing organizations to collaboratively train AI models on decentralized data while maintaining privacy. It scales across devices or siloed data environments without requiring data centralization.

### Q: How does NVFlare protect data privacy?
A: By keeping raw data on local devices and only sharing encrypted model updates, NVFlare minimizes exposure of sensitive information during training. This approach adheres to privacy regulations like GDPR and HIPAA.

### Q: Is NVFlare free to use?
A: Yes, NVFlare is free and open-source software distributed under Apache License 2.0, granting users full rights to run, study, modify, and distribute the code.

### Q: What are NVFlare's key technical features?
A: It provides a runtime environment for orchestrating federated learning jobs, handles secure communication between clients and servers, and supports integration with NVIDIA's AI ecosystem for accelerated computing.

## Why It Matters
NVFlare addresses a critical challenge in AI development: enabling collaborative model training without compromising data privacy. In healthcare, finance, and other regulated industries, it allows organizations to leverage diverse data silos while complying with privacy laws. By open-sourcing the framework, NVIDIA democratizes access to federated learning capabilities, accelerating research and adoption. Its integration with GPU computing infrastructure also addresses performance bottlenecks in distributed training, making large-scale federated learning practically feasible for real-world applications.

## Notable For
- Rapid iteration with multiple stable releases (2.0.0 to 2.0.16) within months of initial launch.
- Comprehensive simulation-to-real-world deployment pipeline, bridging research gaps.
- Industry-specific support for healthcare, finance, and IoT sectors where data privacy is paramount.
- Seamless integration with NVIDIA's AI stack (CUDA, cuDNN) for optimized performance.

## Body
### Architecture
NVFlare operates as a client-server framework for federated learning. Clients (edge devices or organizations) train models locally using their data, then send encrypted updates to a central server for aggregation. The server coordinates training rounds while maintaining global model coherence.

### Licensing
- **License**: Apache Software License 2.0  
  - Permits commercial use, modification, and distribution  
  - Requires attribution and disclosure of source code  
  - Includes patent grant protection  

### Version History
- **Stable Releases**:  
  - 2.0.0 (Released: 2021-11-23)  
  - 2.0.1 (Released: 2021-11-25)  
  - 2.0.2 (Released: 2021-12-02)  
  - 2.0.3 (Released: 2021-12-28)  
  - 2.0.4 (Released: 2022-01-05)  
  - 2.0.5 (Released: 2022-01-10)  
  - 2.0.6 (Released: 2022-01-22)  
  - 2.0.14 (Released: 2022-02-17)  
  - 2.0.16 (Released: 2022-03-17)  

### Technical Specifications
- **Primary Language**: Python  
- **Supported Frameworks**: TensorFlow, PyTorch, ONNX  
- **Communication Protocol**: HTTPS for secure client-server interaction  
- **Security Features**: Homomorphic encryption support, role-based access control  

### Research Foundations
The framework is documented in the peer-reviewed paper "NVIDIA FLARE: Federated Learning from Simulation to Real-World," which details its design methodology and validation through real-world deployments across medical imaging and financial services.

## References

1. [Release 2.0.0. 2021](https://github.com/NVIDIA/NVFlare/releases/tag/2.0.0)
2. [Release 2.0.1. 2021](https://github.com/NVIDIA/NVFlare/releases/tag/2.0.1)
3. [Release 2.0.2. 2021](https://github.com/NVIDIA/NVFlare/releases/tag/2.0.2)
4. [Release 2.0.3. 2021](https://github.com/NVIDIA/NVFlare/releases/tag/2.0.3)
5. [Release 2.0.4. 2022](https://github.com/NVIDIA/NVFlare/releases/tag/2.0.4)
6. [Release 2.0.5. 2022](https://github.com/NVIDIA/NVFlare/releases/tag/2.0.5)
7. [Release 2.0.6. 2022](https://github.com/NVIDIA/NVFlare/releases/tag/2.0.6)
8. [Release 2.0.14. 2022](https://github.com/NVIDIA/NVFlare/releases/tag/2.0.14)
9. [Release 2.0.15. 2022](https://github.com/NVIDIA/NVFlare/releases/tag/2.0.15)
10. [Release 2.0.16. 2022](https://github.com/NVIDIA/NVFlare/releases/tag/2.0.16)
11. [Release 2.1.2. 2022](https://github.com/NVIDIA/NVFlare/releases/tag/2.1.2)
12. [Release 2.1.3. 2022](https://github.com/NVIDIA/NVFlare/releases/tag/2.1.3)
13. [Release 2.2.0. 2022](https://github.com/NVIDIA/NVFlare/releases/tag/2.2.0)
14. [Release 2.2.1. 2022](https://github.com/NVIDIA/NVFlare/releases/tag/2.2.1)
15. [Release 2.2.2. 2022](https://github.com/NVIDIA/NVFlare/releases/tag/2.2.2)
16. [Release 2.2.3. 2023](https://github.com/NVIDIA/NVFlare/releases/tag/2.2.3)
17. [Release 2.2.4. 2023](https://github.com/NVIDIA/NVFlare/releases/tag/2.2.4)
18. [Release 2.2.5. 2023](https://github.com/NVIDIA/NVFlare/releases/tag/2.2.5)
19. [Release 2.2.6. 2023](https://github.com/NVIDIA/NVFlare/releases/tag/2.2.6)
20. [Release 2.3.0. 2023](https://github.com/NVIDIA/NVFlare/releases/tag/2.3.0)
21. [Release 2.3.1. 2023](https://github.com/NVIDIA/NVFlare/releases/tag/2.3.1)
22. [Release 2.3.2. 2023](https://github.com/NVIDIA/NVFlare/releases/tag/2.3.2)
23. [Release 2.3.3. 2023](https://github.com/NVIDIA/NVFlare/releases/tag/2.3.3)
24. [Release 2.3.4. 2023](https://github.com/NVIDIA/NVFlare/releases/tag/2.3.4)
25. [Release 2.3.5. 2023](https://github.com/NVIDIA/NVFlare/releases/tag/2.3.5)
26. [Release 2.3.6. 2023](https://github.com/NVIDIA/NVFlare/releases/tag/2.3.6)
27. [Release 2.3.7. 2023](https://github.com/NVIDIA/NVFlare/releases/tag/2.3.7)
28. [Release 2.3.8. 2023](https://github.com/NVIDIA/NVFlare/releases/tag/2.3.8)
29. [Release 2.3.9. 2023](https://github.com/NVIDIA/NVFlare/releases/tag/2.3.9)
30. [Release 2.3.10. 2024](https://github.com/NVIDIA/NVFlare/releases/tag/2.3.10)
31. [Release 2.3.11. 2024](https://github.com/NVIDIA/NVFlare/releases/tag/2.3.11)
32. [Release 2.3.12. 2024](https://github.com/NVIDIA/NVFlare/releases/tag/2.3.12)
33. [Release 2.4.0. 2024](https://github.com/NVIDIA/NVFlare/releases/tag/2.4.0)
34. [Release 2.4.1. 2024](https://github.com/NVIDIA/NVFlare/releases/tag/2.4.1)
35. [Release 2.4.2. 2024](https://github.com/NVIDIA/NVFlare/releases/tag/2.4.2)
36. [Release 2.5.0. 2024](https://github.com/NVIDIA/NVFlare/releases/tag/2.5.0)
37. [Release 2.5.1. 2024](https://github.com/NVIDIA/NVFlare/releases/tag/2.5.1)
38. [Release 2.5.2. 2024](https://github.com/NVIDIA/NVFlare/releases/tag/2.5.2)
39. [Release 2.6.0. 2025](https://github.com/NVIDIA/NVFlare/releases/tag/2.6.0)
40. [Release 2.6.1. 2025](https://github.com/NVIDIA/NVFlare/releases/tag/2.6.1)
41. [Release 2.6.2. 2025](https://github.com/NVIDIA/NVFlare/releases/tag/2.6.2)
42. [Release 2.7.0. 2025](https://github.com/NVIDIA/NVFlare/releases/tag/2.7.0)
43. [Release 2.7.1. 2025](https://github.com/NVIDIA/NVFlare/releases/tag/2.7.1)
44. [Release 2.6.3. 2025](https://github.com/NVIDIA/NVFlare/releases/tag/2.6.3)
45. [Release 2.7.2. 2026](https://github.com/NVIDIA/NVFlare/releases/tag/2.7.2)
46. [Source](https://api.github.com/repos/NVIDIA/NVFlare)