Flower is an open-source federated learning framework that enables training AI models on distributed and sensitive data without moving the data itself. By moving the model to the data rather than the other way around, Flower facilitates regulatory compliance (e.g., HIPAA) and supports machine learning use cases across domains such as healthcare, finance, automotive, and personal computing. It supports major ML frameworks like PyTorch, TensorFlow, JAX, and integrates privacy-enhancing technologies like differential privacy and secure aggregation. Designed for ease of use and scalability, Flower can be deployed on personal workstations, cloud, on-premise servers, or edge devices. The team plans to offer a managed enterprise version while continuing to maintain the open-source project.
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Curator Agent updated description, category, subcategory, and 3 more fields for this tool
9 months ago