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Flower Framework

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Tutorial

What is Federated Learning?
Get started with Flower
Use a federated learning strategy
Build a strategy from scratch
Customize the client

Quickstart tutorials

Quickstart PyTorch
Quickstart TensorFlow
Quickstart MLX
Quickstart 🤗 Transformers
Quickstart JAX
Quickstart Pandas
Quickstart fastai
Quickstart PyTorch Lightning
Quickstart scikit-learn
Quickstart XGBoost
Quickstart Android
Quickstart iOS

How-to guides

Install Flower
Configure Clients
Design stateful ClientApps
Use strategies
Implement strategies
Aggregate evaluation results
Save and Load Model Checkpoints
Run simulations
Run Flower with the Deployment Engine
Enable TLS connections
Authenticate SuperNodes
Configure logging
Use Built-in Mods
Use Differential Privacy
Implement FedBN
Run Flower on Azure
Use CLI JSON output
Run Flower using Docker
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Upgrade to Flower 1.0
Upgrade to Flower 1.13

Explanations

Federated evaluation
Differential Privacy
Flower Architecture

API reference

flwr
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- client
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  - start_client
  - start_numpy_client
  - Client
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  - NumPyClient
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  - array_from_numpy
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  - ClientMessage
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  - Config
  - ConfigsRecord
  - Context
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Flower CLI reference

Reference docs

Example projects
Telemetry
Changelog
Flower Network Communication
Exit Codes
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FAQ

Contributor tutorials

Contribute on GitHub
Get started as a contributor

Contributor how-to guides

Install development versions
Set up a virtual env
Develop in VSCode Dev Containers
Write documentation
Release Flower
Contribute translations
How to Build Docker Flower Images Locally

Contributor explanations

Public and private APIs

Contributor references

Good first contributions
Secure Aggregation Protocols

Versions

v1.8.0
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v1.10.0
v1.11.0
v1.11.1
v1.12.0
v1.13.0
v1.13.1
v1.14.0
v1.15.0
v1.15.1
v1.15.2
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array_from_numpy¶

array_from_numpy(ndarray: ndarray[Any, dtype[Any]]) → Array[source]¶: Create Array from NumPy ndarray.

bytes_to_ndarray

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array_from_numpy
- array_from_numpy()