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When we call ``start_simulation``, we tell Flower that there are 10 clients (``num_clients=10``). Flower then goes ahead an asks the ``FedAvg`` strategy to select clients. ``FedAvg`` knows that it should select 100% of the available clients (``fraction_fit=1.0``), so it goes ahead and selects 10 random clients (i.e., 100% of 10).
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We now have the class ``FlowerClient`` which defines client-side training/evaluation and ``client_fn`` which allows Flower to create ``FlowerClient`` instances whenever it needs to call ``fit`` or ``evaluate`` on one particular client. The last step is to start the actual simulation using ``flwr.simulation.start_simulation``.
现在我们有了定义客户端训练/评估的类 ``FlowerClient`` 和允许 Flower 在需要调用某个客户端的 ``fit` 或 ``evaluate` 时创建 ``FlowerClient`` 实例的 ``client_fn` 类。最后一步是使用 ``flwr.simulation.start_simulation`` 启动实际模拟。
The function ``start_simulation`` accepts a number of arguments, amongst them the ``client_fn`` used to create ``FlowerClient`` instances, the number of clients to simulate (``num_clients``), the number of federated learning rounds (``num_rounds``), and the strategy. The strategy encapsulates the federated learning approach/algorithm, for example, *Federated Averaging* (FedAvg).
函数 ``start_simulation`` 接受许多参数，其中包括用于创建 ``FlowerClient`` 实例的 ``client_fn``、要模拟的客户端数量（``num_clients``）、联邦学习轮数（``num_rounds``）和策略。策略封装了联邦学习方法/算法，例如*联邦平均* (FedAvg)。
Flower has a number of built-in strategies, but we can also use our own strategy implementations to customize nearly all aspects of the federated learning approach. For this example, we use the built-in ``FedAvg`` implementation and customize it using a few basic parameters. The last step is the actual call to ``start_simulation`` which - you guessed it - starts the simulation:
Flower 有许多内置策略，但我们也可以使用自己的策略实现来定制联邦学习方法的几乎所有方面。在本例中，我们使用内置的 ``FedAvg`` 实现，并使用一些基本参数对其进行定制。最后一步是实际调用 ``start_simulation``开始模拟：
Behind the scenes
幕后
So how does this work? How does Flower execute this simulation?
那么它是如何工作的呢？Flower 如何进行模拟？
When we call ``start_simulation``, we tell Flower that there are 10 clients (``num_clients=10``). Flower then goes ahead an asks the ``FedAvg`` strategy to select clients. ``FedAvg`` knows that it should select 100% of the available clients (``fraction_fit=1.0``), so it goes ahead and selects 10 random clients (i.e., 100% of 10).
当我们调用 ``start_simulation`` 时，我们会告诉 Flower 有 10 个客户（`num_clients=10``）。然后，Flower 会要求 ``FedAvg`` 策略选择客户。``FedAvg`` 知道它应该选择 100%的可用客户（``fraction_fit=1.0``），所以它会随机选择 10 个客户（即 10 的 100%）。
Flower then asks the selected 10 clients to train the model. When the server receives the model parameter updates from the clients, it hands those updates over to the strategy (*FedAvg*) for aggregation. The strategy aggregates those updates and returns the new global model, which then gets used in the next round of federated learning.
然后，Flower 会要求选定的 10 个客户端对模型进行训练。服务器收到客户端的模型参数更新后，会将这些更新交给策略（*FedAvg*）进行聚合。策略会聚合这些更新并返回新的全局模型，然后将其用于下一轮联邦学习。
Where's the accuracy?
准确度在哪里找？
You may have noticed that all metrics except for ``losses_distributed`` are empty. Where did the ``{"accuracy": float(accuracy)}`` go?
您可能已经注意到，除了 ``losses_distributed`` 以外，所有指标都是空的。{"准确度": float(准确度)}``去哪儿了？
Flower can automatically aggregate losses returned by individual clients, but it cannot do the same for metrics in the generic metrics dictionary (the one with the ``accuracy`` key). Metrics dictionaries can contain very different kinds of metrics and even key/value pairs that are not metrics at all, so the framework does not (and can not) know how to handle these automatically.
Flower 可以自动汇总单个客户端返回的损失值，但无法对通用度量字典中的度量进行同样的处理（即带有 "准确度 "键的度量字典）。度量值字典可以包含非常不同种类的度量值，甚至包含根本不是度量值的键/值对，因此框架不知道（也无法知道）如何自动处理这些度量值。
As users, we need to tell the framework how to handle/aggregate these custom metrics, and we do so by passing metric aggregation functions to the strategy. The strategy will then call these functions whenever it receives fit or evaluate metrics from clients. The two possible functions are ``fit_metrics_aggregation_fn`` and ``evaluate_metrics_aggregation_fn``.
作为用户，我们需要告诉框架如何处理/聚合这些自定义指标，为此，我们将指标聚合函数传递给策略。然后，只要从客户端接收到拟合或评估指标，策略就会调用这些函数。两个可能的函数是 ``fit_metrics_aggregation_fn`` 和 ``evaluate_metrics_aggregation_fn``。

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Translation added

  1. Flower Docs
  2. Framework
  3. Chinese (Simplified)
When we call ``start_simulation``, we tell Flower that there are 10 clients (``num_clients=10``). Flower then goes ahead an asks the ``FedAvg`` strategy to select clients. ``FedAvg`` knows that it should select 100% of the available clients (``fraction_fit=1.0``), so it goes ahead and selects 10 random clients (i.e., 100% of 10).
当我们调用 ``start_simulation`` 时，我们会告诉 Flower 有 10 个客户（`num_clients=10``）。然后，Flower 会要求 ``FedAvg`` 策略选择客户。``FedAvg`` 知道它应该选择 100%的可用客户（``fraction_fit=1.0``），所以它会随机选择 10 个客户（即 10 的 100%）。
4 months ago
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  1. Flower Docs
  2. Framework
  3. Chinese (Simplified)
When we call ``start_simulation``, we tell Flower that there are 10 clients (``num_clients=10``). Flower then goes ahead an asks the ``FedAvg`` strategy to select clients. ``FedAvg`` knows that it should select 100% of the available clients (``fraction_fit=1.0``), so it goes ahead and selects 10 random clients (i.e., 100% of 10).
 
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