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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

How-to Guides

  • Build
    • Install Flower
    • Configure pyproject.toml
    • Configure Clients
    • Design stateful ClientApps
    • Use strategies
    • Implement strategies
    • Aggregate evaluation results
    • Save and Load Model Checkpoints
    • Use Built-in Mods
    • Use Differential Privacy
    • Implement FedBN
    • Use CLI JSON output
    • Passe à Flower 1.0
    • Upgrade to Flower 1.13
  • Simulate
    • Run simulations
  • Deploy
    • Run Flower with the Deployment Engine
    • Enable TLS connections
    • Authenticate SuperNodes
    • Configure logging
    • Run Flower on GCP
    • Run Flower on Azure
    • Authenticate Users via OpenID Connect
    • Configure Audit Logging
    • Run Flower using Docker
      • Quickstart with Docker
      • Enable TLS for Secure Connections
      • Persist the State of the SuperLink
      • Set Environment Variables
      • Run with Root User Privileges
      • Run ServerApp or ClientApp as a Subprocess
      • Pin a Docker Image to a Specific Version
      • Use a Different Flower Version
      • Quickstart with Docker Compose
      • Run Flower Quickstart Examples with Docker Compose
      • Deploy Flower on Multiple Machines with Docker Compose
    • Run Flower using Helm
      • Deploy SuperLink
      • Deploy SuperNode

Explications

  • Évaluation fédérée
  • Differential Privacy
  • Architecture florale

References

  • Reference
    • flwr
      • client
        • start_client
        • start_numpy_client
        • Client
        • ClientApp
        • NumPyClient
        • mod
          • adaptiveclipping_mod
          • arrays_size_mod
          • fixedclipping_mod
          • make_ffn
          • message_size_mod
          • secagg_mod
          • secaggplus_mod
          • LocalDpMod
      • commun
        • array_from_numpy
        • bytes_to_ndarray
        • configure
        • event
        • log
        • ndarray_to_bytes
        • ndarrays_to_parameters
        • now
        • parameters_to_ndarrays
        • Array
        • ArrayRecord
        • ClientMessage
        • Code
        • Config
        • ConfigRecord
        • ConfigsRecord
        • Context
        • DisconnectRes
        • Error
        • EvaluateIns
        • EvaluateRes
        • EventType
        • FitIns
        • FitRes
        • GetParametersIns
        • GetParametersRes
        • GetPropertiesIns
        • GetPropertiesRes
        • Message
        • MessageType
        • MessageTypeLegacy
        • Métadonnées
        • MetricRecord
        • Metrics
        • MetricsRecord
        • NDArray
        • NDArrays
        • Parameters
        • ParametersRecord
        • Properties
        • ReconnectIns
        • RecordDict
        • RecordSet
        • ServerMessage
        • Status
      • serveur
        • start_server
        • ClientManager
        • Driver
        • Grid
        • History
        • LegacyContext
        • Serveur
        • ServerApp
        • ServerAppComponents
        • ServerConfig
        • SimpleClientManager
        • strategy
          • Bulyan
          • DPFedAvgAdaptive
          • DPFedAvgFixed
          • DifferentialPrivacyClientSideAdaptiveClipping
          • DifferentialPrivacyClientSideFixedClipping
          • DifferentialPrivacyServerSideAdaptiveClipping
          • DifferentialPrivacyServerSideFixedClipping
          • FaultTolerantFedAvg
          • FedAdagrad
          • FedAdam
          • FedAvg
          • FedAvgAndroid
          • FedAvgM
          • FedMedian
          • FedOpt
          • FedProx
          • FedTrimmedAvg
          • FedXgbBagging
          • FedXgbCyclic
          • FedXgbNnAvg
          • FedYogi
          • Krum
          • QFedAvg
          • Strategy
        • workflow
          • DefaultWorkflow
          • SecAggPlusWorkflow
          • SecAggWorkflow
      • simulation
        • run_simulation
        • run_simulation_process
        • start_simulation
        • SimulationIoConnection
    • Flower CLI reference
    • Example projects
    • Télémétrie
    • Changelog
    • Flower Runtime Comparison
    • Flower Network Communication
    • Exit Codes
      • [0] SUCCESS
      • [1] GRACEFUL_EXIT_SIGINT
      • [2] GRACEFUL_EXIT_SIGQUIT
      • [3] GRACEFUL_EXIT_SIGTERM
      • [100] SUPERLINK_THREAD_CRASH
      • [101] SUPERLINK_LICENSE_INVALID
      • [102] SUPERLINK_LICENSE_MISSING
      • [103] SUPERLINK_LICENSE_URL_INVALID
      • [300] SUPERNODE_REST_ADDRESS_INVALID
      • [301] SUPERNODE_NODE_AUTH_KEYS_REQUIRED
      • [302] SUPERNODE_NODE_AUTH_KEYS_INVALID
      • [600] COMMON_ADDRESS_INVALID
      • [601] COMMON_MISSING_EXTRA_REST
      • [602] COMMON_TLS_NOT_SUPPORTED
    • FAQ

Contributor docs

  • Contribute
    • Contribuer sur GitHub
    • Devenez un·e contributeur·ice
    • 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
    • APIs publiques et privées
    • Bonnes premières contributions
    • Protocoles d’agrégation sécurisés
  • 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
  • v1.15.0
  • v1.15.1
  • v1.15.2
  • v1.16.0
  • v1.17.0
  • v1.18.0
  • v1.19.0
  • v1.20.0
  • main
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Build Flower apps¶

Guides¶

Guides orientés sur la résolutions étapes par étapes de problèmes ou objectifs specifiques.

  • Install Flower
  • Configure pyproject.toml
  • Configure Clients
  • Design stateful ClientApps
  • Use strategies
  • Implement strategies
  • Aggregate evaluation results
  • Save and Load Model Checkpoints
  • Use Built-in Mods
  • Use Differential Privacy
  • Implement FedBN
  • Use CLI JSON output
  • Passe à Flower 1.0
  • Upgrade to Flower 1.13
Next
Install Flower
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Quickstart iOS
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