The latest in Federated Learning with Flower
Announcing Flower 1.14
The Flower Team is excited to announce the release of Flower 1.14 stable 🎉
Chong Shen Ng
Announcing Flower Datasets 0.5.0
The Flower Team is excited to announce the release of Flower Datasets 0.5.0 🎉
Adam Narożniak
Federated Datasets in Research
An overview of the datasets used in FL research.
Announcing Flower 1.13.1
The Flower Team is excited to announce the release of Flower 1.13.1 stable 🎉
Heng Pan
Announcing Flower 1.13
The Flower Team is excited to announce the release of Flower 1.13 stable 🎉
Announcing Flower Datasets 0.4.0
The Flower Team is excited to announce the release of Flower Datasets 0.4.0 🎉
Announcing FlowerTune LLM Leaderboard
The Flower Team is excited to announce the FlowerTune LLM Leaderboard
Yan Gao
Announcing Flower 1.12
The Flower Team is excited to announce the release of Flower 1.12 stable 🎉
Announcing Flower 1.11.1
The Flower Team is excited to announce the release of Flower 1.11.1 stable 🎉
Charles Beauville
Announcing Flower 1.11
The Flower Team is excited to announce the release of Flower 1.11 stable 🎉
Daniel Nata Nugraha
Announcing Flower Datasets 0.3.0
The Flower Team is excited to announce the release of Flower Datasets 0.3.0 🎉
Announcing Flower 1.10
The Flower Team is excited to announce the release of Flower 1.10 stable 🎉
In the Jungle of Federated Learning Frameworks
A comparison of open-source FL frameworks, and new FL comparison suite.
Pascal Riedel
Announcing Flower Datasets 0.2.0
The Flower Team is excited to announce the release of Flower Datasets 0.2.0 🎉
Announcing Flower Node Authentication
We are announcing the new Flower node authentication feature in Flower 1.9
Announcing Flower Docker images
We are excited to announce the release of our new Flower Docker images 🐳
Robert Steiner
Announcing Flower 1.9
The Flower Team is excited to announce the release of Flower 1.9 stable 🎉
Announcing Flower 1.8
The Flower Team is excited to announce the release of Flower 1.8 stable 🎉
Javier Fernandez-Marques
Announcing NVIDIA and Flower Collaboration
NVIDIA and Flower Collaborate to Improve Federated Learning Development for Researchers, Data Scientists and AI Developers.
Holger Roth
Launching Flower Pilot Program: Batch Two
We are launching the call for the next wave of participants in the Flower Pilot Program!
Nicholas Lane
Announcing Flower Discuss
We are happy to announce the launch of our official community forum.
Introducing FlowerLLM
FlowerLLM: The World's first 1.3B parameter LLM trained via Federated Learning
LLM FlowerTune: Federated LLM Fine-tuning with Flower
Check out our new example for federated LLM fine-tuning
Flower中文API文档发布
Flower's Chinese API documentation is now available.
Flower Labs raises $20M Series A
Accelerate the democratization of decentralized and federated AI
Daniel J. Beutel
Federated XGBoost: Flower is all you need
Check out our new features for federated XGBoost
Install Flower with Conda
Flower is now officially distributed on the conda-forge!
Announcing Flower 1.7
The Flower Team is excited to announce the release of Flower 1.7 stable 🎉
Federated Learning with MLX and Flower
MLX is a NumPy-like array framework designed for efficient and flexible machine learning on Apple silicon.
Raspberry Pi 5: Ready for Federated Vision
We benchmarked the new Raspberry Pi 5 against its predecessor on a wide range of new and old vision models.
Federated XGBoost with bagging aggregation
Check out our new examples and docs for federated XGBoost with bagging aggregation
Announcing Flower 1.6
The Flower Team is excited to announce the release of Flower 1.6 stable 🎉
Announcing Flower Datasets
Reliable, thoroughly tested dataset partitioning schemes that create fully reproducible Federated Learning experiments are now possible with Flower Datasets.
Flower User Survey 2023
Are you a Flower user or active in the FL community? Please support us on our journey to improve our framework by completing this survey 🌼
Ruth Galindo
Federated Finetuning of OpenAI's Whisper
Check out the new code example federating OpenAI's Whisper for the downstream task of keyword spotting.
Announcing Flower 1.5
The Flower Team is excited to announce the release of Flower 1.5 stable 🎉
Taner Topal
Federated Learning Standards
A collaborative approach to improving research reproducibility and building an interoperable future.
Patrick Foley
Announcing the Summer of Reproducibility
The Flower Team is excited to announce the Summer of Reproducibility 🏖️
Announcing Flower 1.4
The Flower Team is excited to announce the release of Flower 1.4 stable 🎉
Using XGBoost with Flower 🌳
Check out our new quickstart example leveraging XGBoost to federate learning in a horizontal setting!
Chenyang Ma
Federated Learning with Self-Supervision
Yasar Abbas Ur Rehman explores combining Federated Learning (FL) & Self-Supervised Learning (SSL) for privacy-preserving, decentralized feature learning.
Yasar Abbas Ur Rehman
Announcing Flower Summit 2023
Every year, we bring together the Flower community to exchange ideas and plan for the future. We are excited to announce the Flower Summit 2023!
Announcing Flower Labs
An AI future that is collaborative, open and distributed.
Federated Learning with fastai and Flower
Fast.ai strives to 'make neural nets uncool again' by making it very simple. As you'll see, it's a great match with Flower!
🎒 FL Starter Pack: FedProx on MNIST using a CNN
The MNIST experiment of the paper that demonstrated the FedProx framework is now implemented in baselines!
Announcing the Flower Next Pilot Program
Are you using federated learning to push AI forward? Join the pilot program, work with us to accelerate your project and develop improvements for the entire Flower community.
Announcing Flower 1.3
The Flower Team is excited to announce the release of Flower 1.3 stable
Monitoring Simulation in Flower
Learn how to monitor a simulation's resource consumption to make informed decisions on resource allocation.
Federated Analytics with Flower and Pandas
Federated analytics is the practice of applying data science methods to the analysis of raw data distributed amongst a set of clients.
Announcing Flower 1.2
The Flower Team is excited to announce the release of Flower 1.2 stable
🎒 FL Starter Pack: FedAvg on MNIST using a CNN
New baseline! McMahan et al.'s pioneering FL paper finally added to baselines!
Announcing Flower 1.1
The Flower Team is excited to announce the release of Flower 1.1 stable
Private Ads with Brave & Flower: Call for volunteers in FL user study
We're collaborating with Brave to pioneer FL-based ad serving! Read on to find out how you can help.
Andreea Zaharia
Announcing Flower 1.0
The Flower Team is excited to announce the release of Flower 1.0 stable
Flower 0.19 Release
Flower 0.19 is available! Read on to find out what's new.
Flower´s new Design Language
It´s finally here: Flower´s new design language!
Julian Rußmeyer
JAX meets Flower - Federated Learning with JAX
JAX is a high-performance machine learning framework build by Google researchers. It automatically differentiate and auto-optimizes a function and can now be easliy run federated.
Dr. Maria Börner
Flower 0.18 Release
Flower 0.18 is finally available! Read on to find out what's new.
Federated Learning against Cancer in the Wild: How to Eat an Elephant
Setting up a federated network across clinical centers including clinical requirements to think about.
Akis Linardos
Federated Learning on Android devices with Flower
Federated Learning on Android devices with Flower and TFLite.
Akhil Mathur
Speech models, federated! (SpeechBrain x Flower)
Federated Speech Model Training via SpeechBrain and Flower.
Federated Scikit-learn Using Flower
Scikit-learn models can now be trained on distributed data with Flower.
Kaushik Amar Das
What is the Carbon Footprint of Federated Learning?
Comparing the carbon footprint of centralized and federated machine learning using Flower.
Xinchi Qiu
Running MXNet Federated - MXNet meets Flower
MXNet is a highly efficient and flexible machine learning framework. With Flower you can now build MXNet federated learning workloads for the very first time.
PyTorch: From Centralized To Federated
Federated your existing PyTorch machine learning projects with Flower.
Google Summer of Code: Project Ideas
List of Ideas for Google Summer of Code 2021
Single-Machine Simulation of Federated Learning Systems
How can we simulate a full Federated Learning system on a single machine?
Running Federated Learning applications on Embedded Devices
Federated Learning is catching traction and it is now being used in several commercial applications and services. Check how you can deploy FL applications on Embedded Devices.
Federated Learning in less than 20 lines of code
Can we build a fully-fledged Federated Learning system in less than 20 lines of code? Spoiler alert: yes, we can.
Hello, Flower Blog!
Today we are launching the Flower Blog!
