A few fixes to our Examples

examples/ae_examples/cvae_dim_example/client.py

@@ -15,7 +15,7 @@
 from fl4health.clients.basic_client import BasicClient
 from fl4health.preprocessing.autoencoders.dim_reduction import CvaeFixedConditionProcessor
 from fl4health.utils.config import narrow_dict_type
-from fl4health.utils.load_data import load_mnist_data
+from fl4health.utils.load_data import ToNumpy, load_mnist_data
 from fl4health.utils.metrics import Accuracy, Metric
 from fl4health.utils.random import set_all_random_seeds
 from fl4health.utils.sampler import DirichletLabelBasedSampler
@@ -30,7 +30,7 @@ def get_data_loaders(self, config: Config) -> tuple[DataLoader, DataLoader]:
         batch_size = narrow_dict_type(config, "batch_size", int)
         cvae_model_path = Path(narrow_dict_type(config, "cvae_model_path", str))
         sampler = DirichletLabelBasedSampler(list(range(10)), sample_percentage=0.75, beta=100)
-        transform = transforms.Compose([transforms.ToTensor(), transforms.Lambda(torch.flatten)])
+        transform = transforms.Compose([ToNumpy(), transforms.ToTensor(), transforms.Lambda(torch.flatten)])
         # CvaeFixedConditionProcessor is added to the data transform pipeline to encode the data samples
         train_loader, val_loader, _ = load_mnist_data(
             data_dir=self.data_path,

examples/ae_examples/cvae_dim_example/server.py

@@ -72,6 +72,7 @@ def main(config: dict[str, Any]) -> None:
         fl_config=config,
         strategy=strategy,
         checkpoint_and_state_module=checkpoint_and_state_module,
+        accept_failures=False,
     )
 
     fl.server.start_server(

examples/ae_examples/cvae_examples/conv_cvae_example/client.py

@@ -17,7 +17,7 @@
 from fl4health.preprocessing.autoencoders.loss import VaeLoss
 from fl4health.utils.config import narrow_dict_type
 from fl4health.utils.dataset_converter import AutoEncoderDatasetConverter
-from fl4health.utils.load_data import load_mnist_data
+from fl4health.utils.load_data import ToNumpy, load_mnist_data
 from fl4health.utils.metrics import Metric
 from fl4health.utils.random import set_all_random_seeds
 from fl4health.utils.sampler import DirichletLabelBasedSampler
@@ -60,7 +60,7 @@ def get_data_loaders(self, config: Config) -> tuple[DataLoader, DataLoader]:
         batch_size = narrow_dict_type(config, "batch_size", int)
         sampler = DirichletLabelBasedSampler(list(range(10)), sample_percentage=0.75, beta=100)
         # To make sure pixels stay in the range [0.0, 1.0].
-        transform = transforms.Compose([transforms.ToTensor()])
+        transform = transforms.Compose([ToNumpy(), transforms.ToTensor()])
         # To train an autoencoder-based model we need to set the data converter.
         train_loader, val_loader, _ = load_mnist_data(
             data_dir=self.data_path,

examples/ae_examples/cvae_examples/conv_cvae_example/server.py

@@ -73,6 +73,7 @@ def main(config: dict[str, Any]) -> None:
         fl_config=config,
         strategy=strategy,
         checkpoint_and_state_module=checkpoint_and_state_module,
+        accept_failures=False,
     )
 
     fl.server.start_server(

examples/ae_examples/cvae_examples/mlp_cvae_example/client.py

@@ -17,7 +17,7 @@
 from fl4health.preprocessing.autoencoders.loss import VaeLoss
 from fl4health.utils.config import narrow_dict_type
 from fl4health.utils.dataset_converter import AutoEncoderDatasetConverter
-from fl4health.utils.load_data import load_mnist_data
+from fl4health.utils.load_data import ToNumpy, load_mnist_data
 from fl4health.utils.metrics import Metric
 from fl4health.utils.random import set_all_random_seeds
 from fl4health.utils.sampler import DirichletLabelBasedSampler
@@ -50,7 +50,7 @@ def get_data_loaders(self, config: Config) -> tuple[DataLoader, DataLoader]:
         # ToTensor transform is used to make sure pixels stay in the range [0.0, 1.0].
         # Flattening the image data to match the input shape of the model.
         flatten_transform = transforms.Lambda(lambda x: torch.flatten(x))
-        transform = transforms.Compose([transforms.ToTensor(), flatten_transform])
+        transform = transforms.Compose([ToNumpy(), transforms.ToTensor(), flatten_transform])
         train_loader, val_loader, _ = load_mnist_data(
             data_dir=self.data_path,
             batch_size=batch_size,

examples/ae_examples/cvae_examples/mlp_cvae_example/server.py

@@ -73,6 +73,7 @@ def main(config: dict[str, Any]) -> None:
         fl_config=config,
         strategy=strategy,
         checkpoint_and_state_module=checkpoint_and_state_module,
+        accept_failures=False,
     )
 
     fl.server.start_server(

examples/ae_examples/fedprox_vae_example/client.py

@@ -16,7 +16,7 @@
 from fl4health.preprocessing.autoencoders.loss import VaeLoss
 from fl4health.utils.config import narrow_dict_type
 from fl4health.utils.dataset_converter import AutoEncoderDatasetConverter
-from fl4health.utils.load_data import load_mnist_data
+from fl4health.utils.load_data import ToNumpy, load_mnist_data
 from fl4health.utils.sampler import DirichletLabelBasedSampler
 
 
@@ -25,7 +25,7 @@ def get_data_loaders(self, config: Config) -> tuple[DataLoader, DataLoader]:
         batch_size = narrow_dict_type(config, "batch_size", int)
         sampler = DirichletLabelBasedSampler(list(range(10)), sample_percentage=0.75, beta=100)
         # Flattening the input images to use an MLP-based variational autoencoder.
-        transform = transforms.Compose([transforms.ToTensor(), transforms.Lambda(torch.flatten)])
+        transform = transforms.Compose([ToNumpy(), transforms.ToTensor(), transforms.Lambda(torch.flatten)])
         # Create and pass the autoencoder data converter to the data loader.
         self.autoencoder_converter = AutoEncoderDatasetConverter(condition=None)
         train_loader, val_loader, _ = load_mnist_data(

examples/ae_examples/fedprox_vae_example/config.yaml

@@ -8,7 +8,7 @@ batch_size: 32 # The batch size for client training
 
 # FedProx variables
 adaptive_proximal_weight: False # Whether to use adaptive proximal weight or not
-proximal_weight : 0.1 # The proximal weight
+initial_proximal_weight : 0.1 # The proximal weight
 
 # Checkpointing
 checkpoint_path: "examples/ae_examples/fedprox_vae_example"

examples/ae_examples/fedprox_vae_example/server.py

@@ -66,7 +66,7 @@ def main(config: dict[str, Any]) -> None:
         fit_metrics_aggregation_fn=fit_metrics_aggregation_fn,
         evaluate_metrics_aggregation_fn=evaluate_metrics_aggregation_fn,
         initial_parameters=get_all_model_parameters(model),
-        adapt_loss_weight=config["adapt_proximal_weight"],
+        adapt_loss_weight=config["adaptive_proximal_weight"],
         initial_loss_weight=config["initial_proximal_weight"],
     )
 
@@ -75,6 +75,7 @@ def main(config: dict[str, Any]) -> None:
         fl_config=config,
         strategy=strategy,
         checkpoint_and_state_module=checkpoint_and_state_module,
+        accept_failures=False,
     )
 
     fl.server.start_server(

examples/apfl_example/server.py

@@ -60,7 +60,13 @@ def main(config: dict[str, Any]) -> None:
     )
 
     client_manager = SimpleClientManager()
-    server = FlServer(client_manager=client_manager, fl_config=config, strategy=strategy, reporters=[JsonReporter()])
+    server = FlServer(
+        client_manager=client_manager,
+        fl_config=config,
+        strategy=strategy,
+        reporters=[JsonReporter()],
+        accept_failures=False,
+    )
 
     fl.server.start_server(
         server=server,

examples/basic_example/server.py

@@ -71,6 +71,7 @@ def main(config: dict[str, Any]) -> None:
         fl_config=config,
         strategy=strategy,
         checkpoint_and_state_module=checkpoint_and_state_module,
+        accept_failures=False,
     )
 
     fl.server.start_server(

examples/ditto_example/server.py

@@ -62,7 +62,7 @@ def main(config: dict[str, Any]) -> None:
     )
 
     client_manager = SimpleClientManager()
-    server = DittoServer(client_manager=client_manager, fl_config=config, strategy=strategy)
+    server = DittoServer(client_manager=client_manager, fl_config=config, strategy=strategy, accept_failures=False)
 
     fl.server.start_server(
         server=server,

examples/dp_fed_examples/client_level_dp/server.py

@@ -84,6 +84,7 @@ def main(config: dict[str, Any]) -> None:
         strategy=strategy,
         server_noise_multiplier=config["server_noise_multiplier"],
         num_server_rounds=config["n_server_rounds"],
+        accept_failures=False,
     )
 
     fl.server.start_server(

examples/dp_fed_examples/client_level_dp_weighted/server.py

@@ -80,6 +80,7 @@ def main(config: dict[str, Any]) -> None:
         clipping_noise_multiplier=config["clipping_bit_noise_multiplier"],
         beta=config["server_momentum"],
         weighted_aggregation=config["weighted_averaging"],
+        accept_failures=False,
     )
 
     server = ClientLevelDPFedAvgServer(

examples/dp_fed_examples/instance_level_dp/server.py

@@ -106,6 +106,7 @@ def main(config: dict[str, Any]) -> None:
         batch_size=config["batch_size"],
         num_server_rounds=config["n_server_rounds"],
         checkpoint_and_state_module=checkpoint_and_state_module,
+        accept_failures=False,
     )
 
     fl.server.start_server(

examples/dp_scaffold_example/server.py

@@ -68,6 +68,7 @@ def main(config: dict[str, Any]) -> None:
         num_server_rounds=config["n_server_rounds"],
         strategy=strategy,
         warm_start=True,
+        accept_failures=False,
     )
 
     fl.server.start_server(

examples/dynamic_layer_exchange_example/server.py

@@ -77,7 +77,7 @@ def main(config: dict[str, Any]) -> None:
     )
 
     client_manager = SimpleClientManager()
-    server = FlServer(client_manager=client_manager, fl_config=config, strategy=strategy)
+    server = FlServer(client_manager=client_manager, fl_config=config, strategy=strategy, accept_failures=False)
 
     fl.server.start_server(
         server=server,

examples/feature_alignment_example/server.py

@@ -62,6 +62,7 @@ def main(config: dict[str, Any]) -> None:
         initialize_parameters=get_initial_model_parameters,
         strategy=strategy,
         tabular_features_source_of_truth=tab_feature_info_encoder_hospital1,
+        accept_failures=False,
     )
 
     fl.server.start_server(

examples/fedbn_example/server.py

@@ -70,7 +70,7 @@ def main(config: dict[str, Any], server_address: str, dataset_name: str) -> None
     )
 
     client_manager = SimpleClientManager()
-    server = FlServer(client_manager=client_manager, fl_config=config, strategy=strategy)
+    server = FlServer(client_manager=client_manager, fl_config=config, strategy=strategy, accept_failures=False)
 
     fl.server.start_server(
         server=server,

examples/feddg_ga_example/server.py

@@ -70,7 +70,13 @@ def main(config: dict[str, Any]) -> None:
     # will return the same sampling until it is told to reset, which in FedDgGaStrategy
     # is done right before fit_round.
     client_manager = FixedSamplingClientManager()
-    server = FlServer(client_manager=client_manager, fl_config=config, strategy=strategy, reporters=[JsonReporter()])
+    server = FlServer(
+        client_manager=client_manager,
+        fl_config=config,
+        strategy=strategy,
+        reporters=[JsonReporter()],
+        accept_failures=False,
+    )
 
     fl.server.start_server(
         server=server,

examples/federated_eval_example/server.py

@@ -23,6 +23,7 @@ def main(config: dict[str, Any], server_checkpoint_path: Path | None) -> None:
         evaluate_config=evaluate_config,
         evaluate_metrics_aggregation_fn=uniform_evaluate_metrics_aggregation_fn,
         min_available_clients=config["n_clients"],
+        accept_failures=False,
     )
 
     fl.server.start_server(

examples/fedopt_example/server.py

@@ -147,10 +147,11 @@ def main(config: dict[str, Any]) -> None:
         on_evaluate_config_fn=fit_config_fn,
         # Server side weight initialization
         initial_parameters=get_all_model_parameters(initial_model),
+        accept_failures=False,
     )
 
     client_manager = SimpleClientManager()
-    server = FlServer(client_manager=client_manager, fl_config=config, strategy=strategy)
+    server = FlServer(client_manager=client_manager, fl_config=config, strategy=strategy, accept_failures=False)
 
     fl.server.start_server(
         server_address=config["server_address"],

examples/fedpca_examples/dim_reduction/client.py

@@ -14,7 +14,7 @@
 from fl4health.clients.basic_client import BasicClient
 from fl4health.preprocessing.pca_preprocessor import PcaPreprocessor
 from fl4health.utils.config import narrow_dict_type
-from fl4health.utils.load_data import get_train_and_val_mnist_datasets
+from fl4health.utils.load_data import ToNumpy, get_train_and_val_mnist_datasets
 from fl4health.utils.metrics import Accuracy
 from fl4health.utils.random import set_all_random_seeds
 from fl4health.utils.sampler import DirichletLabelBasedSampler
@@ -31,6 +31,7 @@ def get_data_loaders(self, config: Config) -> tuple[DataLoader, DataLoader]:
         # Get training and validation datasets.
         transform = transforms.Compose(
             [
+                ToNumpy(),
                 transforms.ToTensor(),
                 transforms.Normalize((0.5), (0.5)),
             ]

examples/fedpca_examples/dim_reduction/server.py

@@ -72,6 +72,7 @@ def main(config: dict[str, Any]) -> None:
         fl_config=config,
         strategy=strategy,
         checkpoint_and_state_module=checkpoint_and_state_module,
+        accept_failures=False,
     )
 
     fl.server.start_server(

examples/fedper_example/server.py

@@ -68,7 +68,7 @@ def main(config: dict[str, Any]) -> None:
     )
 
     client_manager = SimpleClientManager()
-    server = FlServer(client_manager=client_manager, fl_config=config, strategy=strategy)
+    server = FlServer(client_manager=client_manager, fl_config=config, strategy=strategy, accept_failures=False)
 
     fl.server.start_server(
         server=server,

examples/fedpm_example/server.py

@@ -65,6 +65,7 @@ def main(config: dict[str, Any]) -> None:
         initial_parameters=get_all_model_parameters(initial_model),
         # Perform Bayesian aggregation.
         bayesian_aggregation=True,
+        accept_failures=False,
     )
 
     client_manager = SimpleClientManager()

examples/fedprox_example/server.py

@@ -81,7 +81,9 @@ def main(config: dict[str, Any], server_address: str) -> None:
         reporters = [wandb_reporter, json_reporter]
     else:
         reporters = [json_reporter]
-    server = FedProxServer(client_manager=client_manager, fl_config=config, strategy=strategy, reporters=reporters)
+    server = FedProxServer(
+        client_manager=client_manager, fl_config=config, strategy=strategy, reporters=reporters, accept_failures=False
+    )
 
     fl.server.start_server(
         server=server,

examples/fedrep_example/server.py

@@ -70,7 +70,7 @@ def main(config: dict[str, Any]) -> None:
     )
 
     client_manager = SimpleClientManager()
-    server = FlServer(client_manager=client_manager, fl_config=config, strategy=strategy)
+    server = FlServer(client_manager=client_manager, fl_config=config, strategy=strategy, accept_failures=False)
 
     fl.server.start_server(
         server=server,

examples/fedsimclr_example/fedsimclr_finetuning_example/client.py

@@ -15,7 +15,7 @@
 from fl4health.model_bases.fedsimclr_base import FedSimClrModel
 from fl4health.utils.config import narrow_dict_type
 from fl4health.utils.dataset import TensorDataset
-from fl4health.utils.load_data import get_cifar10_data_and_target_tensors, split_data_and_targets
+from fl4health.utils.load_data import ToNumpy, get_cifar10_data_and_target_tensors, split_data_and_targets
 from fl4health.utils.metrics import Accuracy
 
 
@@ -26,6 +26,7 @@ def get_finetune_dataset(data_dir: Path, batch_size: int) -> tuple[DataLoader, D
 
     input_transform = transforms.Compose(
         [
+            ToNumpy(),
             transforms.ToTensor(),
             transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
         ]

examples/fedsimclr_example/fedsimclr_finetuning_example/server.py

@@ -76,6 +76,7 @@ def main(config: dict[str, Any]) -> None:
         fl_config=config,
         strategy=strategy,
         checkpoint_and_state_module=checkpoint_and_state_module,
+        accept_failures=False,
     )
 
     fl.server.start_server(

examples/fedsimclr_example/fedsimclr_pretraining_example/client.py

@@ -34,7 +34,6 @@ def get_transforms() -> tuple[Callable, Callable]:
 
     target_transform = transforms.Compose(
         [
-            ToNumpy(),
             transforms.ToPILImage(),
             transforms.RandomHorizontalFlip(p=0.5),
             transforms.RandomApply([color_jitter], p=0.8),

examples/fedsimclr_example/fedsimclr_pretraining_example/server.py

@@ -75,6 +75,7 @@ def main(config: dict[str, Any]) -> None:
         fl_config=config,
         strategy=strategy,
         checkpoint_and_state_module=checkpoint_and_state_module,
+        accept_failures=False,
     )
 
     fl.server.start_server(

examples/fenda_ditto_example/server.py

@@ -68,7 +68,7 @@ def main(config: dict[str, Any]) -> None:
     )
 
     client_manager = SimpleClientManager()
-    server = FlServer(client_manager=client_manager, fl_config=config, strategy=strategy)
+    server = FlServer(client_manager=client_manager, fl_config=config, strategy=strategy, accept_failures=False)
 
     fl.server.start_server(
         server=server,

examples/fenda_example/server.py

@@ -64,7 +64,7 @@ def main(config: dict[str, Any]) -> None:
     )
 
     client_manager = SimpleClientManager()
-    server = FlServer(client_manager=client_manager, fl_config=config, strategy=strategy)
+    server = FlServer(client_manager=client_manager, fl_config=config, strategy=strategy, accept_failures=False)
 
     fl.server.start_server(
         server=server,

examples/fl_plus_local_ft_example/server.py

@@ -54,7 +54,7 @@ def main(config: dict[str, Any]) -> None:
     )
 
     client_manager = SimpleClientManager()
-    server = FlServer(client_manager=client_manager, fl_config=config, strategy=strategy)
+    server = FlServer(client_manager=client_manager, fl_config=config, strategy=strategy, accept_failures=False)
 
     fl.server.start_server(
         server_address="0.0.0.0:8080",

examples/flash_example/server.py

@@ -45,7 +45,7 @@ def main(config: dict[str, Any]) -> None:
     )
 
     client_manager = SimpleClientManager()
-    server = FlServer(client_manager=client_manager, fl_config=config, strategy=strategy)
+    server = FlServer(client_manager=client_manager, fl_config=config, strategy=strategy, accept_failures=False)
 
     fl.server.start_server(
         server=server,