nvflare.private.fed.server.fed_server module

class BaseServer(project_name=None, min_num_clients=2, max_num_clients=10, heart_beat_timeout=600, handlers: List[FLComponent] | None = None, shutdown_period=30.0)

Bases: ABC

Base server that provides the clients management and server deployment.

client_cleanup()

close()

Shutdown the server.

deploy(args, grpc_args=None, secure_train=False)

Start a grpc server and listening the designated port.

fl_shutdown()

get_all_clients() → Dict[str, Client]

Get the list of registered clients.

Returns:

client}

Return type:

A dict of {client_token

get_cell()

logout_client(token)

notify_dead_client(client)

Called to do further processing of the dead client

Parameters:

client – the dead client

Returns:

abstract remove_client_data(token)

remove_dead_clients()

set_admin_server(admin_server)

class FederatedServer(project_name=None, min_num_clients=2, max_num_clients=10, cmd_modules=None, heart_beat_timeout=600, handlers: List[FLComponent] | None = None, args=None, secure_train=False, snapshot_persistor=None, overseer_agent=None, shutdown_period=30.0, check_engine_frequency=3.0)

Bases: BaseServer

Federated server services.

Parameters:

• project_name – server project name.

• min_num_clients – minimum number of contributors at each round.

• max_num_clients – maximum number of contributors at each round.

• cmd_modules – command modules.

• heart_beat_timeout – heartbeat timeout

• handlers – A list of handler

• args – arguments

• secure_train – whether to use secure communication

abort_run()

authentication_check(request: Message, state_check)

client_challenge(request: Message) → Message

client_heartbeat(request: Message) → Message

close()

Shutdown the server.

create_job_cell(job_id, root_url, parent_url, secure_train, server_config) → Cell

create_run_manager(workspace, job_id)

deploy(args, grpc_args=None, secure_train=False)

Start a grpc server and listening the designated port.

fl_shutdown()

notify_dead_client(client)

Called to do further processing of the dead client

Parameters:

client – the dead client

Returns:

overseer_callback(overseer_agent)

process_job_failure(request: Message)

quit_client(request: Message) → Message

Existing client quits the federated training process.

Server will stop sharing the global model with the client, further contribution will be rejected.

This function does not change min_num_clients and max_num_clients.

register_client(request: Message) → Message

Register a new client. Each client must be registered before being able to run jobs.

remove_client_data(token)

reset_tokens()

Reset the token set.

After resetting, each client can take a token and start fetching the current global model. This function is not thread-safe.

run_engine()

sign_auth_token(client_name: str, token: str)

start_run(job_id, run_root, conf, args, snapshot)

stop_run_engine_cell()

stop_training()

task_meta_info(client_name)

Task meta information.

The model_meta_info uniquely defines the current model, it is used to reject outdated client’s update.

verify_auth_token(client_name: str, token: str, signature)