Flower recently launched on Y Combinator's "Launch YC".

Launch YC: Flower: Train AI on distributed data

Flower is an open-source framework for training AI on distributed data using federated learning. Companies like Brave, Banking Circle, and Nokia use Flower to improve their models with sensitive data that they could not leverage before.

"A Friendly Federated Learning Framework"

Founded by Daniel J. Beutel, Taner Topal, and Nic Lane. Daniel previously held roles as Head of AI and CTO and has considerable experience in running and scaling engineering teams. Additionally, he is currently an CS PhD at the University of Cambridge and has an MSc (with distinction) in Software Engineering from the University of Oxford. Taner has held leadership roles in Engineering and as CTO in two previous startups, and his contributions have been used by companies such as Porsche, Lufthansa, and Vattenfall. He also serves as a Visiting Researcher at the University of Cambridge. Nic is a Professor in Machine Learning Systems at the University of Cambridge and has held academic positions at Oxford and UCL. Furthermore, he has extensive industrial research experience, including roles at Microsoft Research and Nokia Bell Labs. Most recently, he co-founded and headed the Samsung AI Center in Cambridge, which employed a staff of 50 and began operations in 2018.


Flower is the leading open-source framework for training better AI on distributed data using federated learning and other privacy-enhancing technologies. Industry leaders use Flower to easily collaborate on model training and are starting to transform high-value verticals like telecommunications (Nokia), healthcare (Korean AI Center for Drug Discovery), finance ([stealth]), automotive (Porsche), and personal computing (Brave). All AI today is based on public data, imagine where AI could be if it used all of the worlds’ distributed private data.

Flower: a unified approach to federated learning, analytics, and evaluation. Federate any workload, any ML framework, and any programming language.

Conventional machine learning needs all data collected in a central place, the motto has always been to “move the data to the computation."

Federated learning can train AI models on distributed and sensitive data by moving the training to the data (instead of moving the data to the training); it just collects the insights from the learning process, and the data stays where it is. 

Flower can train AI on sensitive data spread across organizational silos or user devices to improve models with data that could never be leveraged until now.

Image Credits: Flower

It's more challenging: AI models are moved to data silos or user devices, train locally, send updated models back, aggregate them, and repeat. 

Flower provides the open-source infrastructure to easily use federated learning (and other privacy-enhancing technologies - or short: PETs) with all the tools you know and love today - PyTorch, TensorFlow, JAX, Hugging Face, fastai, Weights & Biases - just bring your existing project, and easily “federate” it using Flower.

Image Credits: Flower

🌍 Scalability

Flower was built to enable real-world systems with a large number of clients. Researchers used Flower to run workloads with tens of millions of clients.

✅ ML Framework Agnostic

Flower is compatible with most existing and future machine learning frameworks. You love Keras? Great. You prefer PyTorch? Awesome. Raw NumPy, no automatic differentiation? You rock!

📱 Cloud, Mobile, Edge & Beyond

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

🚀 Research to Production

Flower enables ideas to start as research projects and then gradually move towards production deployment with low engineering effort and proven infrastructure.

🔗 Platform Independent

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

😃 Usability

It's easy to get started. 20 lines of Python is enough to build a full federated learning system.


🌐 Visit
www.flower.dev to learn more!

⭐️ Give Flower a star on GitHub 

Take the tutorial to learn federated learning in 10 mins + share your feedback with the Flower tea

Register for the Flower Summit 2023

🤔 Interested in working with Flower?
Sign up for the Flower Next Pilot Program

❓Facing challenges in getting the data you need to train good AI models? 

🌼 Join the Flower community on Slack

👍 Follow on 
Twitter & Linkedin.

April 12, 2023
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