def on_configure_evaluate(
self, rnd: int, weights: Weights, client_manager: ClientManager
) -> List[Tuple[ClientProxy, EvaluateIns]]:
"""Configure the next round of evaluation."""
# Do not configure federated evaluation if a centralized evaluation
# function is provided
if self.eval_fn is not None:
return []
# Parameters and config
parameters = weights_to_parameters(weights)
config = {}
if self.on_evaluate_config_fn is not None:
# Custom evaluation config function provided
config = self.on_evaluate_config_fn(rnd)
evaluate_ins = (parameters, config)
# Sample clients
sample_size, min_num_clients = self.num_evaluation_clients(
client_manager.num_available())
clients = client_manager.sample(num_clients=sample_size,
min_num_clients=min_num_clients)
# Return client/config pairs
return [(client, evaluate_ins) for client in clients]
def _one_over_k_sampling(
self, sample_size: int, client_manager: ClientManager
) -> List[ClientProxy]:
"""Sample clients with probability 1/k."""
sample_size, min_num_clients = self.num_fit_clients(
client_manager.num_available()
)
clients = client_manager.sample(
num_clients=sample_size, min_num_clients=min_num_clients
)
return clients
def on_configure_fit(
self, rnd: int, weights: Weights,
client_manager: ClientManager) -> List[Tuple[ClientProxy, FitIns]]:
"""Configure the next round of training."""
self.pre_weights = weights
parameters = weights_to_parameters(weights)
config = {}
if self.on_fit_config_fn is not None:
# Custom fit config function provided
config = self.on_fit_config_fn(rnd)
fit_ins = (parameters, config)
# Sample clients
sample_size, min_num_clients = self.num_fit_clients(
client_manager.num_available())
clients = client_manager.sample(num_clients=sample_size,
min_num_clients=min_num_clients)
# Return client/config pairs
return [(client, fit_ins) for client in clients]