5.1.1.8.1.7. `FedEval.strategy.FederatedStrategy`

5.1.1.8.1.7.1. Module Contents

5.1.1.8.1.7.1.1. Classes

`HostParamsType`	Generic enumeration.
`FedStrategyHostInterface`
`FedStrategyPeerInterface`
`FedStrategyInterface`	the interface of federated strategies.
`FedStrategy`	the basic class of federated strategies.

class FedEval.strategy.FederatedStrategy.HostParamsType

Bases: enum.Enum

Generic enumeration.

Derive from this class to define new enumerations.

Uniform = 'uniform'

Personalized = 'personalized'

class FedEval.strategy.FederatedStrategy.FedStrategyHostInterface

abstract property host_params

Host parameters

Type:: Returns

abstract property host_params_type: Returns the same parameters for all parties or personalized ML model

abstract property stop: Server-side train stop flag

abstract property train_selected_clients: Returns the selected clients for training

abstract property eval_selected_clients: Returns the selected clients for evaluation

abstract retrieve_host_download_info() → Tuple[FedEval.aggregator.ModelWeights, str]

get the host download information,: e.g., model params/weights from its machine/deep learning model.

Raises:: NotImplementedError – raised when called but not overriden.
Returns:: the weights/params of its inner machine/deep learning model. TODO(fgh): the meaning of the str in the return type/
Return type:: ModelWeights

abstract update_host_params(client_params: Iterable[FedEval.aggregator.ModelWeights], aggregate_weights: Iterable[float | int]) → None

update central server’s model params/weights with the aggregated params received from clients.

Parameters:

client_params (Iterable[ModelWeights]) – the weights form different clients, ordered like [params1, params2, …]
aggregate_weights (Iterable[Union[float, int]]) – aggregate weights of different clients, usually set according to the clients’ training sample size. E.g., A, B, and C have 10, 20, and 30 images, then the aggregate_weights can be [1/6, 1/3, 1/2] or [10, 20, 30].

Raises:

NotImplementedError – raised when called but not overriden.

abstract host_exit_job(host)

do self-defined finishing jobs before the shutdown of the central server.

Parameters:: host – TODO(fgh)
Raises:: NotImplementedError – raised when called but not overriden.

abstract host_select_train_clients(ready_clients: List[FedEval.config.ClientId]) → List[FedEval.config.ClientId]

select clients from the given ones for training purpose.

Parameters:: ready_clients (List[ClientId]) – the id list of clients that are ready for training.
Raises:: NotImplementedError – raised when called but not overriden.
Returns:: the id list of the selected clients.
Return type:: List[ClientId]

abstract host_select_evaluate_clients(ready_clients: List[FedEval.config.ClientId]) → List[FedEval.config.ClientId]

select clients from the given ones for evaluation purpose.

Parameters:: ready_clients (List[ClientId]) – the id list of clients that are ready for evaluaion.
Raises:: NotImplementedError – raised when called but not overriden.
Returns:: the id list of the selected clients.
Return type:: List[ClientId]

class FedEval.strategy.FederatedStrategy.FedStrategyPeerInterface

abstract property client_id

Client ID

Type:: Returns

abstract set_host_params_to_local(host_params: FedEval.aggregator.ModelWeights, current_round: int)

update the current local ML/DL model’s params with params received from the central server.

Parameters:

host_params (ModelWeights) – params received from the central server.
current_round (int) – the current round number

Raises:

NotImplementedError – raised when called but not overriden.

abstract fit_on_local_data()

fit model with local data at client side.

Called by the selected clients.

Raises:: NotImplementedError – raised when called but not overriden.
Returns:: TODO(fgh)

abstract retrieve_local_upload_info() → FedEval.aggregator.ModelWeights

return the information aggregated from local model for uploading to the central server.

Called by the selected clients.

Raises:: NotImplementedError – raised when called but not overriden.
Returns:: the local model weights/params.
Return type:: ModelWeights

abstract local_evaluate() → Mapping[str, int | float]

evaluate and test the model received from the central server.

Called by the selected clients.

Raises:: NotImplementedError – raised when called but not overriden.
Returns:: evaluation & test metrics.
Return type:: Mapping[str, Union[int, float]]

abstract client_exit_job(client)

do self-defined finishing jobs before the shutdown of the local clients.

Parameters:: client – TODO(fgh)
Raises:: NotImplementedError – raised when called but not overriden.

class FedEval.strategy.FederatedStrategy.FedStrategyInterface

Bases: FedStrategyHostInterface, FedStrategyPeerInterface

the interface of federated strategies.

This class should be inherited instead of being instantiated.

Raises:: NotImplementedError – raised when called but not overriden.

abstract property param_parser: FedEval.utils.ParamParserInterface

the getter of param_parser.

Raises:: NotImplementedError – raised when called but not overriden.
Returns:: self._param_parser
Return type:: ParamParserInterface

abstract set_logger(logger) → None

the setter of logger of this federated learning.

Parameters:: logger – an external logger.
Raises:: NotImplementedError – raised when called but not overriden.

class FedEval.strategy.FederatedStrategy.FedStrategy(client_id=None, param_parser: type = ParamParser, logger=None)

Bases: FedStrategyInterface

the basic class of federated strategies.

property client_id

Client ID

Type:: Returns

property param_parser: FedEval.utils.ParamParserInterface

the getter of param_parser.

Raises:: NotImplementedError – raised when called but not overriden.
Returns:: self._param_parser
Return type:: ParamParserInterface

property host_params_type: Returns the same parameters for all parties or personalized ML model

property train_selected_clients: Returns the selected clients for training

property host_params

Host parameters

Type:: Returns

property eval_selected_clients: Returns the selected clients for evaluation

property stop: Server-side train stop flag

set_logger(logger) → None

the setter of logger of this federated learning.

Parameters:: logger – an external logger.
Raises:: NotImplementedError – raised when called but not overriden.

_init_model()

_init_data() → None

_init_states()

_config_callback()

_has_callback() → bool

retrieve_host_download_info() → FedEval.aggregator.ModelWeights

get the host download information,: e.g., model params/weights from its machine/deep learning model.

Raises:: NotImplementedError – raised when called but not overriden.
Returns:: the weights/params of its inner machine/deep learning model. TODO(fgh): the meaning of the str in the return type/
Return type:: ModelWeights

update_host_params(client_params, aggregate_weights) → None

update central server’s model params/weights with the aggregated params received from clients.

Parameters:

client_params (Iterable[ModelWeights]) – the weights form different clients, ordered like [params1, params2, …]
aggregate_weights (Iterable[Union[float, int]]) – aggregate weights of different clients, usually set according to the clients’ training sample size. E.g., A, B, and C have 10, 20, and 30 images, then the aggregate_weights can be [1/6, 1/3, 1/2] or [10, 20, 30].

Raises:

NotImplementedError – raised when called but not overriden.

host_exit_job(host)

do self-defined finishing jobs before the shutdown of the central server.

Parameters:: host – TODO(fgh)
Raises:: NotImplementedError – raised when called but not overriden.

host_select_train_clients(ready_clients: List[FedEval.config.ClientId]) → List[FedEval.config.ClientId]

select clients from the given ones for training purpose.

Parameters:: ready_clients (List[ClientId]) – the id list of clients that are ready for training.
Raises:: NotImplementedError – raised when called but not overriden.
Returns:: the id list of the selected clients.
Return type:: List[ClientId]

host_select_evaluate_clients(ready_clients: List[FedEval.config.ClientId]) → List[FedEval.config.ClientId]

select clients from the given ones for evaluation purpose.

Parameters:: ready_clients (List[ClientId]) – the id list of clients that are ready for evaluaion.
Raises:: NotImplementedError – raised when called but not overriden.
Returns:: the id list of the selected clients.
Return type:: List[ClientId]

set_host_params_to_local(host_params: FedEval.aggregator.ModelWeights, current_round: int)

update the current local ML/DL model’s params with params received from the central server.

Parameters:

host_params (ModelWeights) – params received from the central server.
current_round (int) – the current round number

Raises:

NotImplementedError – raised when called but not overriden.

fit_on_local_data()

fit model with local data at client side.

Called by the selected clients.

Raises:: NotImplementedError – raised when called but not overriden.
Returns:: TODO(fgh)

_retrieve_local_params()

retrieve_local_upload_info() → FedEval.aggregator.ModelWeights

return the information aggregated from local model for uploading to the central server.

Called by the selected clients.

Raises:: NotImplementedError – raised when called but not overriden.
Returns:: the local model weights/params.
Return type:: ModelWeights

local_evaluate() → Mapping[str, int | float]

evaluate and test the model received from the central server.

Called by the selected clients.

Raises:: NotImplementedError – raised when called but not overriden.
Returns:: evaluation & test metrics.
Return type:: Mapping[str, Union[int, float]]

client_exit_job(client)

do self-defined finishing jobs before the shutdown of the local clients.

Parameters:: client – TODO(fgh)
Raises:: NotImplementedError – raised when called but not overriden.

5.1.1.8.1.7. FedEval.strategy.FederatedStrategy

5.1.1.8.1.7.1. Module Contents

5.1.1.8.1.7.1.1. Classes

5.1.1.8.1.7. `FedEval.strategy.FederatedStrategy`