fedn.network.combiner.hooks package

The FEDn Hooks package responsible for executing user defined code on the server.

Submodules

fedn.network.combiner.hooks.allowed_import module

fedn.network.combiner.hooks.grpc_wrappers module

fedn.network.combiner.hooks.grpc_wrappers.call_with_fallback(name, fn, *, retries=2, base_sleep=0.25, fallback_fn=None)

fedn.network.combiner.hooks.grpc_wrappers.safe_streaming(func_name)

fedn.network.combiner.hooks.grpc_wrappers.safe_unary(func_name, default_resp_factory)

fedn.network.combiner.hooks.hook_client module

class fedn.network.combiner.hooks.hook_client.CombinerHookInterface

Bases: object

Combiner to server function hooks client.

__init__()

Initialize CombinerHookInterface client.

aggregate(previous_global, update_handler: UpdateHandler, helper, delete_models: bool)

Aggregation call to the hook functions. Sends models in chunks, then asks for aggregation.

Parameters:

global_model – The global model that will be distributed to clients.

Returns:

config that will be distributed to clients.

Return type:

dict

client_selection(clients: list) → list

client_settings(global_model) → dict

Communicates to hook container to get a client config.

Parameters:

global_model – The global model that will be distributed to clients.

Returns:

config that will be distributed to clients.

Return type:

dict

provided_functions(server_functions: str)

Communicates to hook container and asks which functions are available.

Parameters:

server_functions – String version of an implementation of the ServerFunctionsBase interface.

Returns:

dictionary specifing which functions are implemented.

Return type:

dict

fedn.network.combiner.hooks.hooks module

class fedn.network.combiner.hooks.hooks.FunctionServiceServicer

Bases: FunctionServiceServicer

Function service running in an environment combined with each combiner.

Receiving requests from the combiner.

HandleAggregation(request, context)

Missing associated documentation comment in .proto file.

HandleClientConfig(request, context)

Missing associated documentation comment in .proto file.

HandleClientSelection(request, context)

Missing associated documentation comment in .proto file.

HandleMetadata(request, context)

Missing associated documentation comment in .proto file.

HandleProvidedFunctions(request: ProvidedFunctionsResponse, context)

Handles the ‘provided_functions’ request. Sends back which functions are available.

Parameters:

• request (fedn.network.grpc.fedn_pb2.fedn.ProvidedFunctionsRequest) – the provided function request

• context (grpc._server._Context) – the context (unused)

Returns:

dict with str -> bool for which functions are available

Return type:

fedn.network.grpc.fedn_pb2.ProvidedFunctionsResponse

HandleStoreModel(request, context)

Missing associated documentation comment in .proto file.

__init__() → None

Initialize long-running Function server.

check_incremental_aggregate(client_id)

fedn.network.combiner.hooks.hooks.serve()

Start the hooks service.

fedn.network.combiner.hooks.serverfunctionsbase module

class fedn.network.combiner.hooks.serverfunctionsbase.ServerFunctions

Bases: ServerFunctionsBase

class fedn.network.combiner.hooks.serverfunctionsbase.ServerFunctionsBase

Bases: ABC

Base class that defines the structure for the Server Functions. Override these functions to add to the server workflow.

__init__() → None

Initialize the ServerFunctionsBase class. This method can be overridden by subclasses if initialization logic is required.

aggregate(previous_global: List[ndarray], client_updates: Dict[str, Tuple[List[ndarray], Dict]]) → List[ndarray]

Aggregates a list of parameters from clients.

Args:

previous_global (list[np.ndarray]): A list of parameters representing the global model from the previous round.

client_updates (Dict[str, Tuple[List[np.ndarray], Dict]]): A dictionary where the key is client ID, pointing to a tuple with the first element being client parameter and second element being the clients metadata.

Returns:

list[np.ndarray]: A list of numpy arrays representing the aggregated parameters across all clients.

client_selection(client_ids: List[str]) → List

Returns a list of client_id’s of which clients to be used for the next training request.

Args:

client_ids (list[str]): A list of client_ids for all connected clients.

Returns:

list[str]: A list of client ids for which clients should be chosen for the next training round.

client_settings(global_model: List[ndarray]) → Dict

Returns metadata related to the model, which gets distributed to the clients. The dictionary may only contain primitive types.

Args:

global_model (list[np.ndarray]): A list of parameters representing the global model for the upcomming round.

Returns:

dict: A dictionary containing metadata information, supporting only primitive python types.

get_incremental_aggregate_model() → List[ndarray]

Returns the current running model.

Returns:

• list[np.ndarray] (A list of numpy arrays representing the aggregated)

• parameters across all clients.

incremental_aggregate(client_id: str, model: List[ndarray], client_metadata: Dict, previous_global: List[ndarray])

Aggregates a list of parameters from clients.

Args:

client_id: str: the id of the client sending the model.

model (list[np.ndarray]): A list of parameters representing a model as numpy arrays.

client_metadata (Dict): A dictionary containing metadata from the client update.

previous_global (list[np.ndarray]): A list of parameters representing the previous global model as numpy arrays.

Returns:

list[np.ndarray]: A list of numpy arrays representing the aggregated parameters across all clients.

fedn.network.combiner.hooks.serverfunctionstest module

Helper function to test if your server functions implementation runs correctly.

fedn.network.combiner.hooks.serverfunctionstest.test_server_functions(server_functions: ServerFunctionsBase, parameters_np: List[ndarray], client_metadata: Dict, rounds, num_clients)

Test if your functionalities are working on your server functions implementation. :param server_functions: An implementation of ServerFunctionsBase. :type server_functions: ServerFunctionsBase :param parameters: Model parameters in a list of numpy arrays. :type parameters: List[np.ndarray]