Flower
Unified federated learning platform for any workload and ML framework.
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is Flower?
Flower is a unified approach to federated learning that supports analytics and evaluation across various workloads, ML frameworks, and programming languages. It enables developers to federate their models without being constrained by specific technologies or environments.
Key differentiator
“Flower stands out by offering a flexible, unified approach to federated learning that supports any workload and ML framework, making it ideal for projects requiring adaptability across diverse environments.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns, TypeScript SDK is community-maintained
v0.1 to v0.2 migration required rewriting chain definitions
Primary support is for Python-based frameworks like TensorFlow and PyTorch, other languages have limited or no official support
Setting up secure federated learning in a production environment requires significant configuration and infrastructure management
Fit analysis
Who is it for?
✓ Best for
Teams working on federated learning projects who need flexibility in choosing ML frameworks and programming languages.
Projects requiring distributed training without centralized data storage.
Developers looking to evaluate federated learning setups across different environments.
✕ Not a fit for
Scenarios where real-time collaboration is required as Flower focuses more on batch processing.
Teams that prefer a cloud-hosted solution over self-hosting their federated learning infrastructure.
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Alternatives
Works well with
Next step
Get Started with Flower
Step-by-step setup guide with code examples and common gotchas.