Flower: A Friendly Federated Learning Framework

A unified approach to federated learning. Federate any workload, any ML framework, and any programming language.

pip install flwr
A Friendly Federated Learning Framework

Why Flower?

Scalability

Flower was built to encounter real-world setups with a large number of clients. Researchers used Flower to run workloads with more than 10.000 clients.

ML Frameworks

Flower is compatible with a wide range of existing and future Machine Learning frameworks. You love Keras? Great. You prefer PyTorch? Awesome. Raw NumPy, no automatic differentiation? You rock!

Mobile, Edge & Beyond

Flower enables research on all kinds of servers and devices, including mobile. Android, iOS, Raspberry Pi, Nvidia Jetson, all compatible with Flower.

Proven Infrastructure

Flower provides federated learning infrastructure to ensure low engineering effort which enables you to concentrate on your own ML use case.

Platform Independent

Flower is interoperable with different operating systems and hardware platforms to work well heterogeneous edge device environments.

Usability

It's easy to get started. 20 lines of Python is enough to build a full federated learning system in Keras. See Keras Quickstart or PyTorch Quickstart to get started.