def test_load_data_integration(self): """Test partition function.""" # Execute for num_partitions in [10, 100]: for fraction in [ 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 ]: (_, _), _ = load_data(fraction, num_partitions)
def main() -> None: """Load data, create and start client.""" args = parse_args() client_setting = get_client_setting(args.setting, args.cid) # Configure logger configure(identifier=f"client:{client_setting.cid}", host=args.log_host) # Load model model = keyword_cnn(input_shape=(80, 40, 1), seed=SEED) # Load local data partition ( (xy_train_partitions, xy_test_partitions), _, ) = tf_hotkey_partitioned.load_data( iid_fraction=client_setting.iid_fraction, num_partitions=client_setting.num_clients, ) (x_train, y_train) = xy_train_partitions[client_setting.partition] (x_test, y_test) = xy_test_partitions[client_setting.partition] if client_setting.dry_run: x_train = x_train[0:100] y_train = y_train[0:100] x_test = x_test[0:50] y_test = y_test[0:50] # Start client client = VisionClassificationClient( client_setting.cid, model, (x_train, y_train), (x_test, y_test), client_setting.delay_factor, 10, normalization_factor=100.0, ) fl.client.start_client(args.server_address, client)
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 log(INFO, "server_setting: %s", server_setting) # Load evaluation data (_, _), (x_test, y_test) = tf_hotkey_partitioned.load_data(iid_fraction=0.0, num_partitions=1) if server_setting.dry_run: x_test = x_test[0:50] y_test = y_test[0:50] # Load model (for centralized evaluation) model = keyword_cnn(input_shape=(80, 40, 1), seed=SEED) # Strategy eval_fn = get_eval_fn(model=model, num_classes=10, xy_test=(x_test, y_test)) on_fit_config_fn = get_on_fit_config_fn( lr_initial=server_setting.lr_initial, timeout=server_setting.training_round_timeout, partial_updates=server_setting.partial_updates, ) if server_setting.strategy == "fedavg": strategy = fl.server.strategy.FedAvg( fraction_fit=server_setting.sample_fraction, min_fit_clients=server_setting.min_sample_size, min_available_clients=server_setting.min_num_clients, eval_fn=eval_fn, on_fit_config_fn=on_fit_config_fn, ) if server_setting.strategy == "fast-and-slow": if server_setting.training_round_timeout is None: raise ValueError( "No `training_round_timeout` set for `fast-and-slow` strategy") strategy = fl.server.strategy.FastAndSlow( fraction_fit=server_setting.sample_fraction, min_fit_clients=server_setting.min_sample_size, min_available_clients=server_setting.min_num_clients, eval_fn=eval_fn, on_fit_config_fn=on_fit_config_fn, importance_sampling=server_setting.importance_sampling, dynamic_timeout=server_setting.dynamic_timeout, dynamic_timeout_percentile=0.9, alternating_timeout=server_setting.alternating_timeout, r_fast=1, r_slow=1, t_fast=math.ceil(0.5 * server_setting.training_round_timeout), t_slow=server_setting.training_round_timeout, ) if server_setting.strategy == "fedfs-v0": if server_setting.training_round_timeout is None: raise ValueError( "No `training_round_timeout` set for `fedfs-v0` strategy") t_fast = (math.ceil(0.5 * server_setting.training_round_timeout) if server_setting.training_round_timeout_short is None else server_setting.training_round_timeout_short) strategy = fl.server.strategy.FedFSv0( fraction_fit=server_setting.sample_fraction, min_fit_clients=server_setting.min_sample_size, min_available_clients=server_setting.min_num_clients, eval_fn=eval_fn, on_fit_config_fn=on_fit_config_fn, r_fast=1, r_slow=1, t_fast=t_fast, t_slow=server_setting.training_round_timeout, ) if server_setting.strategy == "qffedavg": strategy = fl.server.strategy.QFedAvg( q_param=0.2, qffl_learning_rate=0.1, fraction_fit=server_setting.sample_fraction, min_fit_clients=server_setting.min_sample_size, min_available_clients=server_setting.min_num_clients, eval_fn=eval_fn, on_fit_config_fn=on_fit_config_fn, ) # Run server fl.server.start_server( DEFAULT_SERVER_ADDRESS, config={"num_rounds": server_setting.rounds}, strategy=strategy, )