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Flower Framework

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Tutoriel

Qu’est-ce que l’apprentissage fédéré ?
Get started with Flower
Use a federated learning strategy
Build a strategy from scratch
Customize the client

Quickstart tutorials

Démarrage rapide de PyTorch
Démarrage rapide de TensorFlow
Quickstart MLX
Démarrage rapide 🤗 Transformateurs
Démarrage rapide de JAX
Démarrage rapide des Pandas
Démarrage rapide fastai
Démarrage rapide de PyTorch Lightning
Démarrage rapide de scikit-learn
Démarrage rapide XGBoost
Quickstart Android
Quickstart iOS

Guides

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

Explications

Évaluation fédérée
Differential Privacy
Architecture florale

Référence pour l'API

flwr
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- client
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  - start_client
  - start_numpy_client
  - Client
  - ClientApp
  - NumPyClient
  - mod
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- commun
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  - array_from_numpy
  - bytes_to_ndarray
  - configure
  - event
  - log
  - ndarray_to_bytes
  - ndarrays_to_parameters
  - now
  - parameters_to_ndarrays
  - Array
  - ClientMessage
  - Code
  - Config
  - ConfigsRecord
  - Context
  - DisconnectRes
  - Error
  - EvaluateIns
  - EvaluateRes
  - EventType
  - FitIns
  - FitRes
  - GetParametersIns
  - GetParametersRes
  - GetPropertiesIns
  - GetPropertiesRes
  - Message
  - MessageType
  - MessageTypeLegacy
  - Métadonnées
  - Metrics
  - MetricsRecord
  - NDArray
  - NDArrays
  - Parameters
  - ParametersRecord
  - Properties
  - ReconnectIns
  - RecordSet
  - ServerMessage
  - Status
- serveur
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  - start_server
  - ClientManager
  - Driver
  - History
  - LegacyContext
  - Serveur
  - ServerApp
  - ServerAppComponents
  - ServerConfig
  - SimpleClientManager
  - strategy
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  - workflow
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- simulation
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Flower CLI reference

Référence pour la documentation

Example projects
Télémétrie
Changelog
FAQ

Contributor tutorials

Contribuer sur GitHub
Devenez un·e contributeur·ice

Contributor how-to guides

Installer les versions de développement de Flower
Mettre un environment virtuel en place
Utiliser les conteneurs VS Code Remote
Rédiger de la documentation
Publier Flower
Contribute translations
How to Build Docker Flower Images Locally

Contributor explanations

Public and private APIs

Contributor references

Bonnes premières contributions
Protocoles d’agrégation sécurisés

Versions

v1.8.0
v1.9.0
v1.10.0
v1.11.0
v1.11.1
v1.12.0
v1.13.0
v1.13.1
v1.14.0
main

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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()