Flower

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.

platform:web form:library form:api form:saas pricing:freemium feature:federated-learning feature:differential-privacy feature:secure-aggregation feature:scalable feature:modular feature:open-source feature:cross-framework target:developers target:researchers target:enterprises target:healthcare target:finance use-case:privacy-preserving-ml use-case:distributed-learning use-case:healthcare use-case:finance use-case:automotive language:python

Features

Federated Learning

Differential Privacy

Secure Aggregation

Scalable

Modular

Open Source

Cross Framework

Testimonies

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Basic Info

Category Artificial Intelligence

Website GitHub Doc

Availability & Pricing

Code Access Open Source
Pricing Model
Freemium

AI Curation

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