Flower SuperGrid

The Industry Standard for Enterprise-Grade Federated AI

The Best Organizations Use Flower

TUM
MIT
Harvard
University of Cambridge
Owkin
Mozilla
J.P. Morgan
Banking Circle
NHS
Gachon
Flower Foundation Model

Breakthrough Decentralized Training Research

Our pioneering research in Decentralized Foundation Model Training is the key to unlocking a new pre-training paradigm.

Get Started

Build your first federated learning project in two steps. Use Flower with your favorite machine learning framework to easily federated existing projects.

PyTorch
TensorFlow
HuggingFace
JAX
Pandas
fastai
PyTorch-Lightning
scikit-learn
XGBoost
0. Install Flower
pip install flwr[simulation]
1. Create Flower App
flwr new  # Select TensorFlow & follow instructions
2. Run Flower App
flwr run .
Tutorials

Building with Flower is simple

This series of tutorials introduces the fundamentals of Federated Learning and how to implement it with Flower.

Tutorial preview
Beginner

What is Federated AI?

What you'll learn

learn what federated learning is, build your first system in Flower, and gradually extend it. If you work through all parts of the tutorial, you will be able to build advanced federated learning systems that approach the current state of the art in the field.

Course preview
Beginner

Get started with Flower

What you'll learn

Build a federated learning system using the Flower framework, Flower Datasets and PyTorch. In part 1, we use PyTorch for model training and data loading. In part 2, we federate this PyTorch project using Flower.

Global Scale

The backbone for global Federated AI

Flower is the world’s largest community for Federated AI. Our community deploys in every industry, every region and at every scale.

6,800+
AI Researchers & AI Engineers
6,600+
GitHub Stars
2,500+
Dependent Projects
170+
Contributors
Flower community illustration
Expert support & services

Accelerate your Federated AI project with Flower Labs

Flower’s team of experts is here to support your projects from early prototyping to large-scale production deployment.

Services

Flower is the leading federated AI framework, trusted globally by thousands of projects and developers, with deployments across sectors like healthcare, finance, manufacturing, government, consumer devices, and drug discovery.

Support

Flower integrates nicely with existing hardware and software solutions. Flower can run on any hardware and doesn’t lock you into buying from specific hardware vendors. It also lets your AI engineers use any AI software stack they already know and love.

Users love Flower

Read more Stories
Robert Norvill
Robert Norvill
Senior Data Scientist at Banking Circle
“With the long lived SuperLink and SuperNodes, I don't have to worry about complex FL orchestrations anymore.”
Andrew Soltan
Andrew Soltan
Oncology Registrar & Clinical AI Researcher at University of Oxford
“I love Flower and I'm delighted to see all of the new shiny features in Flower. It has come a really long way since we first used it.”
Eden Ruffell
Eden Ruffell
PhD Student at University College London
“Fine-tuning foundation models for my research using Flower has been amazing and I really like the framework as it's made my life so much easier.”
Alessio Mora
Alessio Mora
PhD, University of Bologna
“Using flwr.simulation is great to me. Previously, I usually simulate everything with a plain loop and sequential clients execution; while just using flwr.simulation I can run multiple clients in parallel and fully use my hardware.”
Aashish Kolluri
Aashish Kolluri
PhD Student at National University of Singapore
“The communications between server and clients have been very smooth even when they were on different machines.”
Industry Usage

Explore how Flower unlocks AI across industries

Federated AI use cases illustration
Flower Intelligence

An Open-Source AI Platform to Run LLMs Locally in Your App or Remotely on Flower Confidential Remote Compute.

DeepLearning.AI Courses with Andrew Ng

Learn the basics of Federated Learning in our 1-hour course “Intro to Federated Learning” and expand to “Federated Fine-Tuning of LLMs.

Beginner to Intermediate1 Hour 8 Minutes
Deep Learning Course