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 _contribution_based_sampling(
self, sample_size: int,
client_manager: ClientManager) -> List[ClientProxy]:
"""Sample clients depending on their past contributions."""
# Get all clients and gather their contributions
all_clients: Dict[str, ClientProxy] = client_manager.all()
cid_idx: Dict[int, str] = {}
raw: List[float] = []
for idx, (cid, _) in enumerate(all_clients.items()):
cid_idx[idx] = cid
penalty = 0.0
if cid in self.contributions.keys():
contribs: List[Tuple[int, int, int]] = self.contributions[cid]
penalty = statistics.mean([c / m for _, c, m in contribs])
# `p` should be:
# - High for clients which have never been picked before
# - Medium for clients which have contributed,
# but not used their entire budget
# - Low (but not 0) for clients which have been picked and used their budget
raw.append(1.1 - penalty)
# Sample clients
return normalize_and_sample(
all_clients=all_clients,
cid_idx=cid_idx,
raw=np.array(raw),
sample_size=sample_size,
use_softmax=False,
)
def configure_fit(
self, rnd: int, parameters: Parameters,
client_manager: ClientManager) -> List[Tuple[ClientProxy, FitIns]]:
"""Configure the next round of training."""
config = {}
if self.on_fit_config_fn is not None:
# Custom fit config function provided
config = self.on_fit_config_fn(rnd)
fit_ins = FitIns(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]
def configure_fit(
self, rnd: int, weights: Weights, client_manager: ClientManager
) -> List[Tuple[ClientProxy, FitIns]]:
"""Configure the next round of training."""
# Block until `min_num_clients` are available
sample_size, min_num_clients = self.num_fit_clients(
client_manager.num_available()
)
success = client_manager.wait_for(
num_clients=min_num_clients, timeout=WAIT_TIMEOUT
)
if not success:
# Do not continue if not enough clients are available
log(
INFO,
"FedFS: not enough clients available after timeout %s",
WAIT_TIMEOUT,
)
return []
# Sample clients
clients = self._contribution_based_sampling(
sample_size=sample_size, client_manager=client_manager
)
# Prepare parameters and config
parameters = weights_to_parameters(weights)
config = {}
if self.on_fit_config_fn is not None:
# Use custom fit config function if provided
config = self.on_fit_config_fn(rnd)
# Set timeout for this round
use_fast_timeout = is_fast_round(rnd - 1, self.r_fast, self.r_slow)
config["timeout"] = str(self.t_fast if use_fast_timeout else self.t_slow)
# Fit instructions
fit_ins = FitIns(parameters, config)
# Return client/config pairs
return [(client, fit_ins) for client in clients]
def _fs_based_sampling(self, sample_size: int,
client_manager: ClientManager,
fast_round: bool) -> List[ClientProxy]:
"""Sample clients with 1/k * c/m in fast rounds and 1 - c/m in slow rounds."""
all_clients: Dict[str, ClientProxy] = client_manager.all()
k = len(all_clients)
cid_idx: Dict[int, str] = {}
raw: List[float] = []
for idx, (cid, _) in enumerate(all_clients.items()):
cid_idx[idx] = cid
if cid in self.contributions.keys():
# Previously selected clients
contribs: List[Tuple[int, int, int]] = self.contributions[cid]
# pylint: disable=invalid-name
if self.use_past_contributions:
cs = [c for _, c, _ in contribs]
ms = [m for _, _, m in contribs]
c_over_m = sum(cs) / sum(ms)
else:
_, c, m = contribs[-1]
c_over_m = c / m
# pylint: enable-msg=invalid-name
if fast_round:
importance = (1 / k) * c_over_m + E
else:
importance = 1 - c_over_m + E
else:
# Previously unselected clients
if fast_round:
importance = 1 / k
else:
importance = 1
raw.append(importance)
log(
DEBUG,
"FedFS _fs_based_sampling, sample %s clients, raw %s",
str(sample_size),
str(raw),
)
return normalize_and_sample(
all_clients=all_clients,
cid_idx=cid_idx,
raw=np.array(raw),
sample_size=sample_size,
use_softmax=False,
)
def register_client(
client_manager: ClientManager,
client: GrpcClientProxy,
context: grpc.ServicerContext,
) -> bool:
"""Try registering GrpcClientProxy with ClientManager."""
is_success = client_manager.register(client)
if is_success:
def rpc_termination_callback() -> None:
client.bridge.close()
client_manager.unregister(client)
context.add_callback(rpc_termination_callback)
return is_success
def configure_fit(
self, rnd: int, weights: Weights,
client_manager: ClientManager) -> List[Tuple[ClientProxy, FitIns]]:
"""Configure the next round of training."""
# Block until `min_num_clients` are available
sample_size, min_num_clients = self.num_fit_clients(
client_manager.num_available())
success = client_manager.wait_for(num_clients=min_num_clients,
timeout=WAIT_TIMEOUT)
if not success:
# Do not continue if not enough clients are available
log(
INFO,
"FedFS: not enough clients available after timeout %s",
WAIT_TIMEOUT,
)
return []
# Sample clients
msg = "FedFS round %s, sample %s clients (based on all previous contributions)"
if self.alternating_timeout:
log(
DEBUG,
msg,
str(rnd),
str(sample_size),
)
clients = self._contribution_based_sampling(
sample_size=sample_size, client_manager=client_manager)
elif self.importance_sampling:
if rnd == 1:
# Sample with 1/k in the first round
log(
DEBUG,
"FedFS round %s, sample %s clients with 1/k",
str(rnd),
str(sample_size),
)
clients = self._one_over_k_sampling(
sample_size=sample_size, client_manager=client_manager)
else:
fast_round = is_fast_round(rnd - 1,
r_fast=self.r_fast,
r_slow=self.r_slow)
log(
DEBUG,
"FedFS round %s, sample %s clients, fast_round %s",
str(rnd),
str(sample_size),
str(fast_round),
)
clients = self._fs_based_sampling(
sample_size=sample_size,
client_manager=client_manager,
fast_round=fast_round,
)
else:
clients = self._one_over_k_sampling(sample_size=sample_size,
client_manager=client_manager)
# Prepare parameters and config
parameters = weights_to_parameters(weights)
config = {}
if self.on_fit_config_fn is not None:
# Use custom fit config function if provided
config = self.on_fit_config_fn(rnd)
# Set timeout for this round
if self.dynamic_timeout:
if self.durations:
candidates = timeout_candidates(
durations=self.durations,
max_timeout=self.t_slow,
)
timeout = next_timeout(
candidates=candidates,
percentile=self.dynamic_timeout_percentile,
)
config["timeout"] = str(timeout)
else:
# Initial round has not past durations, use max_timeout
config["timeout"] = str(self.t_slow)
elif self.alternating_timeout:
use_fast_timeout = is_fast_round(rnd - 1, self.r_fast, self.r_slow)
config["timeout"] = str(
self.t_fast if use_fast_timeout else self.t_slow)
else:
config["timeout"] = str(self.t_slow)
# Fit instructions
fit_ins = FitIns(parameters, config)
# Return client/config pairs
return [(client, fit_ins) for client in clients]