使用策略¶
Flower allows full customization of the learning process through the Strategy
abstraction. A number of built-in strategies are provided in the core framework.
有三种方法可以自定义 Flower 在服务器端协调学习过程的方式：
Use an existing strategy, for example,
FedAvg使用回调函数定制现有策略
实施新策略
使用现有策略¶
Flower comes with a number of popular federated learning Strategies which can be instantiated as follows:
from flwr.common import Context
from flwr.server.strategy import FedAvg
from flwr.server import ServerApp, ServerAppComponents, ServerConfig
def server_fn(context: Context):
# Optional context-based parameters specification
num_rounds = context.run_config["num-server-rounds"]
config = ServerConfig(num_rounds=num_rounds)
# Instantiate FedAvg strategy
strategy = FedAvg(
fraction_fit=context.run_config["fraction-fit"],
fraction_evaluate=1.0,
)
# Create and return ServerAppComponents
return ServerAppComponents(strategy=strategy, config=config)
# Create ServerApp
app = ServerApp(server_fn=server_fn)
To make the ServerApp use this strategy, pass a server_fn function to the
ServerApp constructor. The server_fn function should return a
ServerAppComponents object that contains the strategy instance and a
ServerConfig instance.
Both Strategy and ServerConfig classes can be configured with parameters. The
Context object passed to server_fn contains the values specified in the
[tool.flwr.app.config] table in your pyproject.toml (a snippet is shown below).
To access these values, use context.run_config.
# ...
[tool.flwr.app.config]
num-server-rounds = 10
fraction-fit = 0.5
# ...
使用回调函数定制现有策略¶
Existing strategies provide several ways to customize their behavior. Callback functions allow strategies to call user-provided code during execution. This approach enables you to modify the strategy's partial behavior without rewriting the whole class from zero.
配置客户匹配和客户评估¶
The server can pass new configuration values to the client each round by providing a
function to on_fit_config_fn. The provided function will be called by the strategy
and must return a dictionary of configuration key value pairs that will be sent to the
client. It must return a dictionary of arbitrary configuration values client.fit and
client.evaluate functions during each round of federated learning.
from flwr.common import Context
from flwr.server.strategy import FedAvg
from flwr.server import ServerApp, ServerAppComponents, ServerConfig
def get_on_fit_config_fn() -> Callable[[int], Dict[str, str]]:
"""Return a function which returns training configurations."""
def fit_config(server_round: int) -> Dict[str, str]:
"""Return a configuration with static batch size and (local) epochs."""
config = {
"learning_rate": str(0.001),
"batch_size": str(32),
}
return config
return fit_config
def server_fn(context: Context):
# Read num_rounds from context
num_rounds = context.run_config["num-server-rounds"]
config = ServerConfig(num_rounds=num_rounds)
# Instantiate FedAvg strategy
strategy = FedAvg(
fraction_fit=context.run_config["fraction-fit"],
fraction_evaluate=1.0,
on_fit_config_fn=get_on_fit_config_fn(),
)
# Create and return ServerAppComponents
return ServerAppComponents(strategy=strategy, config=config)
# Create ServerApp
app = ServerApp(server_fn=server_fn)
The on_fit_config_fn can be used to pass arbitrary configuration values from server
to client and potentially change these values each round, for example, to adjust the
learning rate. The client will receive the dictionary returned by the
on_fit_config_fn in its own client.fit() function. And while the values can be
also passed directly via the context this function can be a place to implement finer
control over the fit behaviour that may not be achieved by the context, which sets
fixed values.
Similar to on_fit_config_fn, there is also on_evaluate_config_fn to customize
the configuration sent to client.evaluate()
配置服务器端评估¶
Server-side evaluation can be enabled by passing an evaluation function to
evaluate_fn.
实施新策略¶
编写完全自定义的策略涉及的内容较多，但灵活性最高。阅读 `实施策略 <how-to-implement-strategies.html>_ 指南，了解更多信息。