Py之tensorflow-federated：tensorflow-federated的简介、安装、使用方法之详细攻略

Py之tensorflow-federated：tensorflow-federated的简介、安装、使用方法之详细攻略

目录

tensorflow-federated的简介

tensorflow-federated的安装

tensorflow-federated的使用方法

1、基础案例

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tensorflow-federated的简介

          TensorFlow Federated (TFF)是一个用于机器学习和其他分布式数据计算的开源框架。TFF 的开发旨在促进联邦学习 (FL) 的开放研究和实验，这是一种机器学习方法，其中一个共享的全局模型在许多参与的客户之间进行训练，这些客户将他们的训练数据保存在本地。例如，FL 已被用于训练移动键盘的预测模型，而无需将敏感的打字数据上传到服务器。
          TFF使开发人员能够在他们的模型和数据中使用包含的联邦学习算法，以及试验新的算法。TFF提供的构建块还可以用于实现非学习计算，例如对分散数据的聚合分析。
          TFF的接口分为两层:

• Federated Learning (FL) API ：learning 层提供了一组高级接口，允许开发人员将所包含的联邦训练和评估实现应用到他们现有的 TensorFlow 模型中。
• Federated Core (FC) API ：系统的核心是一组低级接口，用于通过在强类型函数式编程环境中将 TensorFlow 与分布式通信运算符相结合，简洁地表达新颖的联合算法。这一层也是我们构建 tff.learning 的基础。

          TFF使开发人员能够声明式地表示联邦计算，因此可以将它们部署到不同的运行时环境中。TFF中包含了一个用于实验的单机模拟运行时。

官方网站：http://tensorflow.org/federated

GitHub官网：GitHub - tensorflow/federated: A framework for implementing federated learning

tensorflow-federated的安装

pip install tensorflow-federated

tensorflow-federated的使用方法

1、基础案例

参考文章：federated/program.py at main · tensorflow/federated · GitHub

import asyncio
import os.path
from typing import Sequence, Tuple, Union

from absl import app
from absl import flags
import tensorflow as tf
import tensorflow_federated as tff

from tensorflow_federated.examples.program import computations
from tensorflow_federated.examples.program import program_logic

_OUTPUT_DIR = flags.DEFINE_string('output_dir', None, 'The output path.')


def _filter_metrics(path: Tuple[Union[str, int], ...]) -> bool:
  if path == (computations.METRICS_TOTAL_SUM,):
    return True
  else:
    return False


def main(argv: Sequence[str]) -> None:
  if len(argv) > 1:
    raise app.UsageError('Too many command-line arguments.')

  total_rounds = 10
  number_of_clients = 3

  # Configure the platform-specific components; in this example, the TFF native
  # platform is used, but this example could use any platform that conforms to
  # the approprate abstract interfaces.

  # Create a context in which to execute the program logic.
  context = tff.google.backends.native.create_local_async_cpp_execution_context(
  )
  context = tff.program.NativeFederatedContext(context)
  tff.framework.set_default_context(context)

  # Create data sources that are compatible with the context and computations.
  to_int32 = lambda x: tf.cast(x, tf.int32)
  datasets = [tf.data.Dataset.range(10).map(to_int32)] * 3
  train_data_source = tff.program.DatasetDataSource(datasets)
  evaluation_data_source = tff.program.DatasetDataSource(datasets)

  # Create computations that are compatible with the context and data sources.
  initialize = computations.initialize
  train = computations.train
  evaluation = computations.evaluation

  # Configure the platform-agnostic components.

  # Create release managers with access to customer storage.
  train_metrics_managers = [tff.program.LoggingReleaseManager()]
  evaluation_metrics_managers = [tff.program.LoggingReleaseManager()]
  model_output_manager = tff.program.LoggingReleaseManager()

  if _OUTPUT_DIR.value is not None:
    summary_dir = os.path.join(_OUTPUT_DIR.value, 'summary')
    tensorboard_manager = tff.program.TensorBoardReleaseManager(summary_dir)
    train_metrics_managers.append(tensorboard_manager)

    csv_path = os.path.join(_OUTPUT_DIR.value, 'evaluation_metrics.csv')
    csv_manager = tff.program.CSVFileReleaseManager(csv_path)
    evaluation_metrics_managers.append(csv_manager)

  # Group the metrics release managers; program logic may accept a single
  # release manager to make the implementation of the program logic simpler and
  # easier to maintain, the program can use a
  # `tff.program.GroupingReleaseManager` to release values to multiple
  # destinations.
  #
  # Filter the metrics before they are released; the program can use a
  # `tff.program.FilteringReleaseManager` to limit the values that are
  # released by the program logic. If a formal privacy guarantee is not
  # required, it may be ok to release all the metrics.
  train_metrics_manager = tff.program.FilteringReleaseManager(
      tff.program.GroupingReleaseManager(train_metrics_managers),
      _filter_metrics)
  evaluation_metrics_manager = tff.program.FilteringReleaseManager(
      tff.program.GroupingReleaseManager(evaluation_metrics_managers),
      _filter_metrics)

  # Create a program state manager with access to platform storage.
  program_state_manager = None

  if _OUTPUT_DIR.value is not None:
    program_state_dir = os.path.join(_OUTPUT_DIR.value, 'program_state')
    program_state_manager = tff.program.FileProgramStateManager(
        program_state_dir)

  # Execute the program logic; the program logic is abstracted into a separate
  # function to illustrate the boundary between the program and the program
  # logic. This program logic is declared as an async def and needs to be
  # executed in an asyncio event loop.
  asyncio.run(
      program_logic.train_federated_model(
          initialize=initialize,
          train=train,
          train_data_source=train_data_source,
          evaluation=evaluation,
          evaluation_data_source=evaluation_data_source,
          total_rounds=total_rounds,
          number_of_clients=number_of_clients,
          train_metrics_manager=train_metrics_manager,
          evaluation_metrics_manager=evaluation_metrics_manager,
          model_output_manager=model_output_manager,
          program_state_manager=program_state_manager))


if __name__ == '__main__':
  app.run(main)