ClientConfig

class fl_sim.nodes.ClientConfig(*args: ~typing.Any, batch_size: int, num_epochs: int, lr: float, optimizer: str, scheduler: dict | None = None, extra_observes: ~typing.List[str] = <factory>, verbose: int = 1, latency: float = 0.0, algorithm: str = '', **kwargs: ~typing.Any)

基类：BaseModel

Configs for the Client.

参数:

• algorithm (str) -- The algorithm name. Defaults to be derived from the class name (with the ClientConfig suffix removed).

• optimizer (str) -- The name of the optimizer to solve the local (inner) problem.

• batch_size (int) -- The batch size.

• num_epochs (int) -- The number of epochs.

• lr (float) -- The learning rate.

• scheduler (dict, optional) -- The scheduler config. None for no scheduler, using constant learning rate.

• extra_observes (List[str], optional) -- Extra attributes to observe during training, which would be recorded in evaluated metrics, sent to the server, and written to the log file.

• verbose (int, default 1) -- The verbosity level.

• latency (float, default 0.0) -- The latency of the client. Not used in the current version, reserved for future use.

• **kwargs (dict, optional) -- The other arguments, will be set as attributes of the class.

• note:: (..) -- Positional arguments are supported for backward compatibility (mapped onto the fields in their declaration order), but keyword arguments are recommended.

extra_repr_keys() → List[str]

Kept for backward compatibility with the former ReprMixin-based configs; the fields are all included in the (pydantic) default representation.

model_config: ClassVar[ConfigDict] = {'extra': 'allow'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].