5.1.1.8.1.6. FedEval.strategy.FedSVD

5.1.1.8.1.6.1. Module Contents

5.1.1.8.1.6.1.1. Classes

FedSVDStatus

Generic enumeration.

FedSVD

the basic class of federated strategies.

5.1.1.8.1.6.1.2. Functions

generate_orthogonal_matrix([n, reuse, block_reduce, ...])

retrieve_array_from_list(q, start, end)

FedEval.strategy.FedSVD.generate_orthogonal_matrix(n=1000, reuse=False, block_reduce=None, random_seed=None, only_inverse=False, file_name=None, offload_to_disk=False, clear_cache=False)
FedEval.strategy.FedSVD.retrieve_array_from_list(q, start, end)
class FedEval.strategy.FedSVD.FedSVDStatus

Bases: enum.Enum

Generic enumeration.

Derive from this class to define new enumerations.

Init = 'init'
SendMask = 'send_mask'
ApplyMask = 'apply_mask'
Factorization = 'factorization'
RemoveMask = 'remove_mask'
Evaluate = 'evaluate'
class FedEval.strategy.FedSVD.FedSVD(*args, **kwargs)

Bases: FedEval.strategy.FederatedStrategy.FedStrategy

the basic class of federated strategies.

_log_hardware_usage()
_init_model()
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]

retrieve_host_download_info()
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, *args)

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.

safe_memmap_matmul(a, b, step_size=1000)
_server_svd(data_matrix)
set_host_params_to_local(host_params, **kwargs)

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.

_generate_masked_data_in_secure_agg(shape, base=None)
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)

local_evaluate()

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.

retrieve_local_upload_info()

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

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.