# flwrlabs/flower

Flower: A Friendly Federated AI Framework

Repository: https://github.com/flwrlabs/flower
Canonical: https://ross.abutalabs.com/products/flwrlabs-flower
Homepage: https://flower.ai
Language: Python
License: Apache-2.0
License Family: permissive
Topics: flower, federated-learning, federated-learning-framework, federated-analytics, fleet-learning, fleet-intelligence, deep-learning, machine-learning, pytorch, scikit-learn, tensorflow, framework, grpc, python, ai, artificial-intelligence, raspberry-pi, android, cpp, ios
Last push: 2026-08-26T21:31:27+00:00

## Health v2 (maintenance only)
Score: 99/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 99, longevity 100
- inputs: {"age_days": 2389, "days_push": 7, "days_rel": 8, "gap_med": 23, "n_releases_24m": 30}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 7085, forks 1221 (observed 2026-08-28T04:09:55.805658+00:00)

## What it is
Flower (flwr) is an open-source Python framework for building federated and collaborative AI systems, supporting any ML framework such as PyTorch, TensorFlow, JAX, and scikit-learn. It includes simulation and deployment runtimes, a hub for sharing federated apps, and an experimental agent runtime for collaborative AI applications.

## Use cases
- train a model across distributed data without centralizing it
- run federated learning simulations locally
- federated fine-tuning of LLMs
- deploy federated learning on mobile devices like Android and iOS
- implement custom federated learning aggregation strategies
- add differential privacy to federated training
- build collaborative AI agents that share context across a federation
- federated analytics with pandas

## When to choose
- you need privacy-preserving training across siloed or edge data
- you want a framework-agnostic federated learning library with strong research extensibility
- you need to simulate federated setups before real deployment
- you target heterogeneous clients including mobile and embedded devices

## When to avoid
- you only need standard centralized training on a single dataset
- you need a fully turnkey production MLOps platform rather than a framework
- you depend on the experimental Flower Agent features in a stability-critical system

## Facets
- artifact type: framework
- maturity: active
- function: machine-learning, deep-learning, llm-training, agent-framework, rpc, sdk
- domain: machine-learning, artificial-intelligence, large-language-models, microservices, privacy
- platform: python, cross-platform, cpp
- tags: federated-learning, federated-analytics, pytorch, tensorflow, scikit-learn, grpc, differential-privacy, simulation, collaborative-ai, ai-agents, android, ios, docker, kubernetes, gpu

## Member repositories
- flwrlabs/flower (main) score 99

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:55.805658+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:40:11.244510+00:00, confidence not recorded.
  - readme: https://github.com/flwrlabs/flower (fetched 2026-08-28T04:09:55.805658+00:00, sha 41de58cc5289)
  - homepage: https://flower.ai (fetched 2026-08-29T08:35:58.633250+00:00, sha 36a236217b13)
  - site_page: https://flower.ai/docs/framework/tutorial-series-what-is-federated-learning.html (fetched 2026-08-29T08:35:58.637541+00:00, sha cbdf0ad0a6a3)
  - site_page: https://flower.ai/docs/agent (fetched 2026-08-29T08:35:58.640702+00:00, sha 93033f5ccbde)
  - site_page: https://flower.ai/docs/framework (fetched 2026-08-29T08:35:58.642665+00:00, sha 7c6afcb7a27d)
  - site_page: https://flower.ai/docs/hub (fetched 2026-08-29T08:35:58.645480+00:00, sha c854269061b4)
  - site_page: https://flower.ai/docs/model (fetched 2026-08-29T08:35:58.647770+00:00, sha 2315b2da9956)
- Data as of 2026-08-30T08:39:29.467469+00:00.