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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 🤗 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
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
Upgrade to Flower 1.0
Upgrade to Flower Next

Legacy example guides

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

Explanations

Federated evaluation
Differential Privacy

API reference

flwr
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- client
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- common
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- server
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  - run_driver_api
  - run_fleet_api
  - run_server_app
  - run_superlink
  - start_server
  - ClientManager
  - Driver
  - History
  - LegacyContext
  - Server
  - ServerApp
  - ServerConfig
  - SimpleClientManager
  - strategy
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  - workflow
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- simulation
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  - start_simulation
  - run_simulation

Flower CLI reference

Reference docs

Example projects
Telemetry
Changelog
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
Creating New Messages
Write documentation
Release Flower
Contribute translations
How to build Docker Flower images locally

Contributor explanations

Flower Architecture

Contributor references

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

Versions

v1.5.0
v1.6.0
v1.7.0
v1.8.0
main

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run_simulation_from_cli#

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