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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
Monitor simulation
Configure logging
Enable SSL connections
Use Built-in Mods
Use Differential Privacy
Authenticate SuperNodes
Run Flower using Docker
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Upgrade to Flower 1.0
Upgrade to Flower Next

Legacy example guides

Example: PyTorch - From Centralized To Federated
Example: FedBN in PyTorch - From Centralized To Federated

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
  - mod
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  - array_from_numpy
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  - Config
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  - Context
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Flower CLI reference
Advanced Commands
Technical Commands
Deprecated Commands

Reference docs

Example projects
Telemetry
Changelog
FAQ

Contributor tutorials

Contribute on GitHub
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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

FED - Flower Enhancement Doc
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- FED Template
- Flower Enhancement Doc
Good first contributions
Secure Aggregation Protocols

Versions

v1.8.0
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v1.11.1
v1.12.0
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ndarray_to_bytes¶

ndarray_to_bytes(ndarray: ndarray[Any, dtype[Any]]) → bytes[source]¶: Serialize NumPy ndarray to bytes.

ndarrays_to_parameters

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ndarray_to_bytes
- ndarray_to_bytes()