def main() -> None:
"""Start server and train a number of rounds."""
args = parse_args()
# Configure logger
configure(identifier="server", host=args.log_host)
server_setting = get_setting(args.setting).server
# Load evaluation data
xy_partitions, xy_test = tf_cifar_partitioned.load_data(
iid_fraction=0.0, num_partitions=1, cifar100=False
)
_, xy_test = load_partition(
xy_partitions,
xy_test,
partition=0,
num_clients=1,
seed=SEED,
dry_run=server_setting.dry_run,
)
# Load model (for centralized evaluation)
model = resnet50v2(input_shape=(32, 32, 3), num_classes=10, seed=SEED)
# Create client_manager, strategy, and server
client_manager = flwr.SimpleClientManager()
strategy = flwr.strategy.DefaultStrategy(
fraction_fit=server_setting.sample_fraction,
min_fit_clients=server_setting.min_sample_size,
min_available_clients=server_setting.min_num_clients,
eval_fn=get_eval_fn(model=model, num_classes=10, xy_test=xy_test),
on_fit_config_fn=get_on_fit_config_fn(
server_setting.lr_initial, server_setting.training_round_timeout
),
)
# strategy = flwr.strategy.FastAndSlow(
# fraction_fit=args.sample_fraction,
# min_fit_clients=args.min_sample_size,
# min_available_clients=args.min_num_clients,
# eval_fn=get_eval_fn(model=model, num_classes=10, xy_test=xy_test),
# on_fit_config_fn=get_on_fit_config_fn(
# args.lr_initial, args.training_round_timeout
# ),
# r_fast=1,
# r_slow=1,
# t_fast=20,
# t_slow=40,
# )
server = flwr.Server(client_manager=client_manager, strategy=strategy)
# Run server
flwr.app.start_server(
DEFAULT_GRPC_SERVER_ADDRESS,
DEFAULT_GRPC_SERVER_PORT,
server,
config={"num_rounds": server_setting.rounds},
)
def main() -> None:
"""Start server and train five rounds."""
parser = argparse.ArgumentParser(description="Flower")
parser.add_argument(
"--grpc_server_address",
type=str,
default=DEFAULT_GRPC_SERVER_ADDRESS,
help="gRPC server address (default: [::])",
)
parser.add_argument(
"--grpc_server_port",
type=int,
default=DEFAULT_GRPC_SERVER_PORT,
help="gRPC server port (default: 8080)",
)
parser.add_argument(
"--rounds",
type=int,
default=1,
help="Number of rounds of federated learning (default: 1)",
)
parser.add_argument(
"--sample_fraction",
type=float,
default=0.1,
help=
"Fraction of available clients used for fit/evaluate (default: 0.1)",
)
parser.add_argument(
"--min_sample_size",
type=int,
default=1,
help="Minimum number of clients used for fit/evaluate (default: 1)",
)
parser.add_argument(
"--min_num_clients",
type=int,
default=1,
help=
"Minimum number of available clients required for sampling (default: 1)",
)
parser.add_argument("--cid", type=str, help="Client CID (no default)")
args = parser.parse_args()
# Load evaluation data
_, xy_test = fashion_mnist.load_data(partition=0, num_partitions=1)
# Create client_manager, strategy, and server
client_manager = fl.SimpleClientManager()
strategy = fl.strategy.DefaultStrategy(
fraction_fit=args.sample_fraction,
min_fit_clients=args.min_sample_size,
min_available_clients=args.min_num_clients,
eval_fn=get_eval_fn(xy_test=xy_test),
on_fit_config_fn=fit_config,
)
server = fl.Server(client_manager=client_manager, strategy=strategy)
# Run server
fl.app.start_server(
args.grpc_server_address,
args.grpc_server_port,
server,
config={"num_rounds": args.rounds},
)