NVIDIA FLARE™ (Federated Learning Application Runtime Environment) is a domain-agnostic, open-source, and extensible SDK designed for federated learning. It allows researchers and data scientists to adapt existing machine learning (ML) and deep learning (DL) workflows to a federated paradigm, enabling platform developers to build secure, privacy-preserving solutions for distributed multi-party collaboration. NVIDIA FLARE facilitates the development and validation of more accurate and generalizable AI models from diverse data sources while mitigating the risk of compromising data security and privacy.

Features

• Privacy-Preserving Algorithms: Ensures each change to the global model remains hidden, preventing the server from reverse-engineering submitted weights and discovering any training data.
• Training and Evaluation Workflows: Built-in paradigms use local and decentralized data to keep models relevant at the edge, including learning algorithms for FedAvg, FedOpt, and FedProx.
• Extensible Management Tools: Secure provisioning using SSL certifications, orchestration through an admin console, and monitoring of federated learning experiments using TensorBoard for visualization.
• Supports Popular ML/DL Frameworks: Flexible design compatible with PyTorch, TensorFlow, and even Numpy, allowing integration of federated learning into current workflows.
• Extensive API: Open-source API enables the development of new federated workflow strategies, innovative learning, and privacy-preserving algorithms.
• Reusable Building Blocks: Facilitates federated learning experiments with reusable components and example walkthroughs.

Benefits

• Enhanced Security: Develop and validate AI models from diverse data sources while ensuring data security and privacy.
• Accelerated AI Research: Adapt existing ML/DL workflows to a federated learning paradigm, accelerating AI research and development.
• Open-Source Framework: General-purpose, domain-agnostic SDK that fosters an ecosystem of developers, researchers, and data scientists.
• Scalability: Suitable for large-scale deployments, ensuring reliable performance in enterprise environments.
• Flexibility: Supports a wide range of ML/DL frameworks and deployment scenarios, from local servers to cloud environments.
• Comprehensive Support: Benefit from extensive management tools and reusable building blocks for streamlined development.