NVIDIA FLARE: Federated Learning from Simulation to Real-World (2210.13291v3)

Abstract: 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.

• The paper introduces a customizable federated learning SDK that integrates with major ML libraries and supports adaptable workflow designs.
• It employs a lightweight, modular architecture featuring robust security measures such as differential privacy, homomorphic encryption, and mutual TLS.
• Its real-world application in healthcare and multinational collaborations highlights the practical benefits of decentralized model training for sensitive data.

An Overview of NVIDIA FLARE: A Federated Learning SDK

The paper introduces NVIDIA FLARE (NVFlare), a comprehensive open-source software development kit (SDK) designed to facilitate federated learning (FL) research and real-world implementation. The SDK offers a robust framework for researchers and enterprises seeking to engage in privacy-preserving machine learning across varied domains, notably healthcare. FL distinguishes itself by enabling collaboration through data-sharing restrictions, offering an alternative to centralized data collection.

NVFlare presents an adaptable environment where machine learning workflows, crafted in popular libraries such as PyTorch, TensorFlow, and even NumPy, can operate within a federated context. This support extends to high-level features like secure communication, multi-tasking capabilities, and server failover solutions, ensuring continuous and reliable learning processes across different infrastructures.

Key Contributions and Features

One of the haLLMark features of NVFlare is its Controller programming API which allows for the customization of federated workflows. As a lightweight and componentized framework, NVFlare provides:

• FL Simulator: Enables rapid prototyping and testing of federated learning models in a controlled environment.
• Dashboard and Monitoring Tools: Facilitates project administration, ensuring secure provisioning and deployment of FL projects.
• Privacy Mechanisms: Integrates methodologies like differential privacy and homomorphic encryption to secure data throughout the learning process.
• Interoperability: Support for integrating with other machine learning frameworks such as MONAI and XGBoost.

System Architecture

NVFlare operates within a client-server architecture which consists of FL servers, multiple clients, and an admin interface. Communication between these components is managed via abstracted communication layers, providing flexibility and customizing opportunities in protocol selection to optimize for performance or security. This flexibility extends to implementing common interaction patterns like broadcast and relay with a thorough API.

Moreover, NVFlare supports dynamic task allocation through Workers and Controllers, optimizing the distributed learning process. By defining tasks and processing results through an event-driven paradigm, it accommodates diverse learning strategies and conditions.

Security and Privacy

Security is an intrinsic component of NVFlare. The SDK includes features for:

• Authentication: Through mutual TLS, ensuring secure identification across participating clients and servers.
• Federated Authorization: Role-based policies dictate what actions can be executed by stakeholders within the system.
• Client Privacy Controls: Briefly manage local policies regarding data encryption and authorization filters to ensure local compliance.

Use Cases and Real-World Applications

NVFlare has been successfully deployed in healthcare settings, demonstrating the potential for FL in multinational partnerships developing models for tasks like medical imaging and predictive analytics in clinical environments. The diversity of applications exemplifies its versatility in addressing various data privacy challenges across sectors.

Implications for Future Research

NVFlare's innovative design has significant implications for AI, both in research and application contexts. By promoting a system that is library-agnostic and conducive to real-world deployment, it enables advancements in federated learning methodologies and fosters collaborative opportunities across domains that require stringent data privacy measures. Future iterations and community contributions are expected to introduce capabilities for handling large-scale models and integrating parameter-efficient fine-tuning strategies.

Conclusion

NVIDIA FLARE emerges as a vital tool in operationalizing federated learning, merging research-grade flexibility with production-ready capabilities. Its open-source nature invites further exploration and collaboration, encouraging the development of novel federated algorithms. The NVFlare SDK positions itself as a crucial component in the field of federated data science, aiming to address the nuances of decentralized model training and its associated challenges.