About
Flower (flwr) is a unified, framework-agnostic platform for federated learning, federated analytics, and federated evaluation. It lets teams train AI models collaboratively across siloed datasets—on edge devices, hospital networks, or global data centers—without raw data ever leaving its source. This privacy-by-design approach is critical in regulated industries like healthcare, finance, and cybersecurity. Flower's simple Python API means you can federate an existing ML project in just a few lines of code. The framework integrates natively with TensorFlow, PyTorch, Hugging Face, and NumPy, and its language-agnostic design supports heterogeneous client environments. The Flower Hub provides a growing library of community-built Flower Apps—ranging from federated survival analysis (FedECA) and phishing URL detection to camera-fleet scene classification—accelerating adoption across industries. For organizations needing more, Flower Labs offers professional services ranging from on-call advisory to full build-and-operate engagements, plus enterprise-grade support covering the entire open-source-to-platform stack. Flower's pioneering research in decentralized foundation model training also opens a new paradigm for pre-training large models without centralized data. With 6,800+ GitHub stars, 2,500+ dependent projects, and 180+ contributors, Flower is the industry standard for federated AI.
Key Features
- Framework-Agnostic Federation: Federate existing ML projects built on TensorFlow, PyTorch, Hugging Face, NumPy, or any other framework with minimal code changes.
- Flower Hub App Marketplace: Discover and publish ready-to-run Flower Apps built by the community, covering domains like healthcare, cybersecurity, and computer vision.
- Decentralized Foundation Model Training: Pioneering research and tooling for training large foundation models in a fully decentralized manner, eliminating the need to centralize massive datasets.
- Enterprise Services & Support: Flower Labs provides professional services from early prototyping to full production deployment, with enterprise-grade support covering the entire stack.
- Simple Getting-Started Experience: Install via pip, scaffold a new Flower App with a single CLI command, and run a federated experiment locally or across distributed nodes within minutes.
Use Cases
- Training clinical AI models across multiple hospitals without sharing patient records, enabling privacy-preserving medical research.
- Federated phishing and cybersecurity threat detection across enterprise networks without exposing sensitive traffic logs.
- Camera-fleet scene classification for autonomous vehicles or smart cities where raw video cannot leave edge devices.
- Collaborative financial fraud detection across banks that must comply with data residency and privacy regulations.
- Academic research into federated learning algorithms, aggregation strategies, and differential privacy using Flower's extensible framework.
Pros
- Truly Framework-Agnostic: Works with virtually every major ML framework and supports multiple programming languages, so teams are never locked into a specific stack.
- Strong Community & Ecosystem: 7,000+ researchers and engineers, 6,800+ GitHub stars, and 2,500+ dependent projects provide rich documentation, tutorials, and ready-made apps.
- Rapid Onboarding: Comprehensive tutorials and a simple pip-based setup allow new users to run their first federated learning experiment in hours, not weeks.
- Production-Ready Enterprise Path: Flower Labs offers professional services and support for organizations that need to scale beyond open-source self-hosting to enterprise-grade deployments.
Cons
- Federated Learning Expertise Required: Getting the most out of Flower requires understanding federated learning concepts—data heterogeneity, aggregation strategies, and privacy guarantees—which can steepen the learning curve.
- Enterprise Features Incur Cost: Advanced support, managed infrastructure, and custom professional services are paid offerings through Flower Labs, which may be a barrier for smaller teams.
- Distributed Infrastructure Overhead: Running multi-client federated experiments requires coordinating distributed compute environments, adding DevOps complexity compared to centralized training.
Frequently Asked Questions
What is federated learning and why does Flower use it?
Federated learning trains machine learning models across multiple decentralized devices or servers that hold local data samples, without exchanging the raw data itself. Flower uses this approach to enable privacy-preserving AI in industries like healthcare, finance, and edge computing where data cannot or should not be centralized.
Which machine learning frameworks does Flower support?
Flower is framework-agnostic and officially supports TensorFlow, PyTorch, Hugging Face Transformers, and NumPy out of the box. Its design also allows integration with virtually any other ML library or custom training loop.
Is Flower free to use?
Yes. The Flower framework is fully open-source (Apache 2.0) and free to use. Flower Labs offers optional paid professional services and enterprise support for organizations that need expert guidance or managed infrastructure.
How do I get started with Flower?
Install Flower with `pip install flwr`, then scaffold a new project using `flwr new` with your chosen ML framework template. The quickstart tutorials on flower.ai walk you through building your first federated system in two steps.
Can Flower be used to train large foundation models?
Yes. Flower's research team has pioneered decentralized foundation model training, enabling large model pre-training across distributed data sources without centralization—opening a new paradigm for privacy-preserving LLM and vision model development.
