diff --git a/examples/flower-client/8b601bcb-0392-44c8-a2f2-2f77b13674d8.tgz b/examples/flower-client/8b601bcb-0392-44c8-a2f2-2f77b13674d8.tgz
deleted file mode 100644
index 557fff559..000000000
Binary files a/examples/flower-client/8b601bcb-0392-44c8-a2f2-2f77b13674d8.tgz and /dev/null differ
diff --git a/examples/flower-client/client275-20240423-182523/entrypoint.py b/examples/flower-client/client275-20240423-182523/entrypoint.py
deleted file mode 100755
index e80e7d4b5..000000000
--- a/examples/flower-client/client275-20240423-182523/entrypoint.py
+++ /dev/null
@@ -1,107 +0,0 @@
-import os
-
-import fire
-from flwr_client import app
-
-from fedn.utils.flowercompat.client_app_adapter import FlwrClientAppAdapter
-from fedn.utils.helpers.helpers import get_helper, save_metadata, save_metrics
-
-HELPER_MODULE = "numpyhelper"
-helper = get_helper(HELPER_MODULE)
-
-flwr_adapter = FlwrClientAppAdapter(app)
-
-
-def _get_node_id():
-    """Get client number from environment variable."""
-
-    number = os.environ.get("CLIENT_NUMBER", "0")
-    return int(number)
-
-
-def save_parameters(out_path, parameters_np):
-    """Save model paramters to file.
-
-    :param model: The model to serialize.
-    :type model: torch.nn.Module
-    :param out_path: The path to save to.
-    :type out_path: str
-    """
-    helper.save(parameters_np, out_path)
-
-
-def init_seed(out_path="../seed.npz"):
-    """Initialize seed model and save it to file.
-
-    :param out_path: The path to save the seed model to.
-    :type out_path: str
-    """
-    # This calls get_parameters in the flower client which needs to be implemented.
-    parameters_np = flwr_adapter.init_parameters(partition_id=_get_node_id(), config={})
-    save_parameters(out_path, parameters_np)
-
-
-def train(in_model_path, out_model_path):
-    """Complete a model update.
-
-    Load model paramters from in_model_path (managed by the FEDn client),
-    perform a model update through the flower client, and write updated paramters
-    to out_model_path (picked up by the FEDn client).
-
-    :param in_model_path: The path to the input model.
-    :type in_model_path: str
-    :param out_model_path: The path to save the output model to.
-    :type out_model_path: str
-    """
-    parameters_np = helper.load(in_model_path)
-
-    # Train on flower client
-    params, num_examples = flwr_adapter.train(
-        parameters=parameters_np, partition_id=_get_node_id(), config={}
-    )
-
-    # Metadata needed for aggregation server side
-    metadata = {
-        # num_examples are mandatory
-        "num_examples": num_examples,
-    }
-
-    # Save JSON metadata file (mandatory)
-    save_metadata(metadata, out_model_path)
-
-    # Save model update (mandatory)
-    save_parameters(out_model_path, params)
-
-
-def validate(in_model_path, out_json_path, data_path=None):
-    """Validate model on the clients test dataset.
-
-    :param in_model_path: The path to the input model.
-    :type in_model_path: str
-    :param out_json_path: The path to save the output JSON to.
-    :type out_json_path: str
-    :param data_path: The path to the data file.
-    :type data_path: str
-    """
-    parameters_np = helper.load(in_model_path)
-
-    loss, accuracy = flwr_adapter.evaluate(parameters_np, partition_id=_get_node_id(), config={})
-
-    # JSON schema
-    report = {
-        "test_loss": loss,
-        "test_accuracy": accuracy,
-    }
-    print(f"Loss: {loss}, accuracy: {accuracy}")
-    # Save JSON
-    save_metrics(report, out_json_path)
-
-
-if __name__ == "__main__":
-    fire.Fire(
-        {
-            "init_seed": init_seed,
-            "train": train,
-            "validate": validate,
-        }
-    )
diff --git a/examples/flower-client/client275-20240423-182523/fedn.yaml b/examples/flower-client/client275-20240423-182523/fedn.yaml
deleted file mode 100644
index 9fb33f18f..000000000
--- a/examples/flower-client/client275-20240423-182523/fedn.yaml
+++ /dev/null
@@ -1,8 +0,0 @@
-python_env: python_env.yaml
-entry_points:
-  build:
-    command: python entrypoint.py init_seed
-  train:
-    command: python entrypoint.py train
-  validate:
-    command: python entrypoint.py validate
\ No newline at end of file
diff --git a/examples/flower-client/client275-20240423-182523/flwr_client.py b/examples/flower-client/client275-20240423-182523/flwr_client.py
deleted file mode 100644
index 297df3ca7..000000000
--- a/examples/flower-client/client275-20240423-182523/flwr_client.py
+++ /dev/null
@@ -1,41 +0,0 @@
-"""Flower client code using the ClientApp abstraction.
-Code adapted from https://github.com/adap/flower/tree/main/examples/app-pytorch.
-"""
-
-from flwr.client import ClientApp, NumPyClient
-from flwr_task import (DEVICE, Net, get_weights, load_data, set_weights, test,
-                       train)
-
-
-# Define FlowerClient and client_fn
-class FlowerClient(NumPyClient):
-    def __init__(self, cid) -> None:
-        super().__init__()
-        self.net = Net().to(DEVICE)
-        self.trainloader, self.testloader = load_data(
-            partition_id=int(cid), num_clients=10
-        )
-
-    def get_parameters(self, config):
-        return [val.cpu().numpy() for _, val in self.net.state_dict().items()]
-
-    def fit(self, parameters, config):
-        set_weights(self.net, parameters)
-        train(self.net, self.trainloader, epochs=3)
-        return get_weights(self.net), len(self.trainloader.dataset), {}
-
-    def evaluate(self, parameters, config):
-        set_weights(self.net, parameters)
-        loss, accuracy = test(self.net, self.testloader)
-        return loss, len(self.testloader.dataset), {"accuracy": accuracy}
-
-
-def client_fn(cid: str):
-    """Create and return an instance of Flower `Client`."""
-    return FlowerClient(cid).to_client()
-
-
-# Flower ClientApp
-app = ClientApp(
-    client_fn=client_fn,
-)
diff --git a/examples/flower-client/client275-20240423-182523/flwr_task.py b/examples/flower-client/client275-20240423-182523/flwr_task.py
deleted file mode 100644
index dea53d9cc..000000000
--- a/examples/flower-client/client275-20240423-182523/flwr_task.py
+++ /dev/null
@@ -1,95 +0,0 @@
-"""Flower client code for helper functions.
-Code adapted from https://github.com/adap/flower/tree/main/examples/app-pytorch.
-"""
-
-from collections import OrderedDict
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from flwr_datasets import FederatedDataset
-from torch.utils.data import DataLoader
-from torchvision.transforms import Compose, Normalize, ToTensor
-from tqdm import tqdm
-
-DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-
-class Net(nn.Module):
-    """Model (simple CNN adapted from 'PyTorch: A 60 Minute Blitz')"""
-
-    def __init__(self) -> None:
-        super(Net, self).__init__()
-        self.conv1 = nn.Conv2d(3, 6, 5)
-        self.pool = nn.MaxPool2d(2, 2)
-        self.conv2 = nn.Conv2d(6, 16, 5)
-        self.fc1 = nn.Linear(16 * 5 * 5, 120)
-        self.fc2 = nn.Linear(120, 84)
-        self.fc3 = nn.Linear(84, 10)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.pool(F.relu(self.conv1(x)))
-        x = self.pool(F.relu(self.conv2(x)))
-        x = x.view(-1, 16 * 5 * 5)
-        x = F.relu(self.fc1(x))
-        x = F.relu(self.fc2(x))
-        return self.fc3(x)
-
-
-def load_data(partition_id, num_clients):
-    """Load partition CIFAR10 data."""
-    fds = FederatedDataset(dataset="cifar10", partitioners={"train": num_clients})
-    partition = fds.load_partition(partition_id)
-    # Divide data on each node: 80% train, 20% test
-    partition_train_test = partition.train_test_split(test_size=0.2)
-    pytorch_transforms = Compose(
-        [ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
-    )
-
-    def apply_transforms(batch):
-        """Apply transforms to the partition from FederatedDataset."""
-        batch["img"] = [pytorch_transforms(img) for img in batch["img"]]
-        return batch
-
-    partition_train_test = partition_train_test.with_transform(apply_transforms)
-    trainloader = DataLoader(partition_train_test["train"], batch_size=32, shuffle=True)
-    testloader = DataLoader(partition_train_test["test"], batch_size=32)
-    return trainloader, testloader
-
-
-def train(net, trainloader, epochs):
-    """Train the model on the training set."""
-    criterion = torch.nn.CrossEntropyLoss()
-    optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
-    for _ in range(epochs):
-        for batch in tqdm(trainloader, "Training"):
-            images = batch["img"]
-            labels = batch["label"]
-            optimizer.zero_grad()
-            criterion(net(images.to(DEVICE)), labels.to(DEVICE)).backward()
-            optimizer.step()
-
-
-def test(net, testloader):
-    """Validate the model on the test set."""
-    criterion = torch.nn.CrossEntropyLoss()
-    correct, loss = 0, 0.0
-    with torch.no_grad():
-        for batch in tqdm(testloader, "Testing"):
-            images = batch["img"].to(DEVICE)
-            labels = batch["label"].to(DEVICE)
-            outputs = net(images)
-            loss += criterion(outputs, labels).item()
-            correct += (torch.max(outputs.data, 1)[1] == labels).sum().item()
-    accuracy = correct / len(testloader.dataset)
-    return loss, accuracy
-
-
-def get_weights(net):
-    return [val.cpu().numpy() for _, val in net.state_dict().items()]
-
-
-def set_weights(net, parameters):
-    params_dict = zip(net.state_dict().keys(), parameters)
-    state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict})
-    net.load_state_dict(state_dict, strict=True)
diff --git a/examples/flower-client/client275-20240423-182523/python_env.yaml b/examples/flower-client/client275-20240423-182523/python_env.yaml
deleted file mode 100644
index 984a2e96d..000000000
--- a/examples/flower-client/client275-20240423-182523/python_env.yaml
+++ /dev/null
@@ -1,11 +0,0 @@
-name: flower-client
-build_dependencies:
-  - pip
-  - setuptools
-  - wheel==0.37.1
-dependencies:
-  - torch==2.2.1
-  - torchvision==0.17.1
-  - fire==0.3.1
-  - fedn[flower]==0.9.0
-  - flwr-datasets[vision]==0.1.0
\ No newline at end of file
diff --git a/examples/flower-client/client275-20240423-182644/entrypoint.py b/examples/flower-client/client275-20240423-182644/entrypoint.py
deleted file mode 100755
index e80e7d4b5..000000000
--- a/examples/flower-client/client275-20240423-182644/entrypoint.py
+++ /dev/null
@@ -1,107 +0,0 @@
-import os
-
-import fire
-from flwr_client import app
-
-from fedn.utils.flowercompat.client_app_adapter import FlwrClientAppAdapter
-from fedn.utils.helpers.helpers import get_helper, save_metadata, save_metrics
-
-HELPER_MODULE = "numpyhelper"
-helper = get_helper(HELPER_MODULE)
-
-flwr_adapter = FlwrClientAppAdapter(app)
-
-
-def _get_node_id():
-    """Get client number from environment variable."""
-
-    number = os.environ.get("CLIENT_NUMBER", "0")
-    return int(number)
-
-
-def save_parameters(out_path, parameters_np):
-    """Save model paramters to file.
-
-    :param model: The model to serialize.
-    :type model: torch.nn.Module
-    :param out_path: The path to save to.
-    :type out_path: str
-    """
-    helper.save(parameters_np, out_path)
-
-
-def init_seed(out_path="../seed.npz"):
-    """Initialize seed model and save it to file.
-
-    :param out_path: The path to save the seed model to.
-    :type out_path: str
-    """
-    # This calls get_parameters in the flower client which needs to be implemented.
-    parameters_np = flwr_adapter.init_parameters(partition_id=_get_node_id(), config={})
-    save_parameters(out_path, parameters_np)
-
-
-def train(in_model_path, out_model_path):
-    """Complete a model update.
-
-    Load model paramters from in_model_path (managed by the FEDn client),
-    perform a model update through the flower client, and write updated paramters
-    to out_model_path (picked up by the FEDn client).
-
-    :param in_model_path: The path to the input model.
-    :type in_model_path: str
-    :param out_model_path: The path to save the output model to.
-    :type out_model_path: str
-    """
-    parameters_np = helper.load(in_model_path)
-
-    # Train on flower client
-    params, num_examples = flwr_adapter.train(
-        parameters=parameters_np, partition_id=_get_node_id(), config={}
-    )
-
-    # Metadata needed for aggregation server side
-    metadata = {
-        # num_examples are mandatory
-        "num_examples": num_examples,
-    }
-
-    # Save JSON metadata file (mandatory)
-    save_metadata(metadata, out_model_path)
-
-    # Save model update (mandatory)
-    save_parameters(out_model_path, params)
-
-
-def validate(in_model_path, out_json_path, data_path=None):
-    """Validate model on the clients test dataset.
-
-    :param in_model_path: The path to the input model.
-    :type in_model_path: str
-    :param out_json_path: The path to save the output JSON to.
-    :type out_json_path: str
-    :param data_path: The path to the data file.
-    :type data_path: str
-    """
-    parameters_np = helper.load(in_model_path)
-
-    loss, accuracy = flwr_adapter.evaluate(parameters_np, partition_id=_get_node_id(), config={})
-
-    # JSON schema
-    report = {
-        "test_loss": loss,
-        "test_accuracy": accuracy,
-    }
-    print(f"Loss: {loss}, accuracy: {accuracy}")
-    # Save JSON
-    save_metrics(report, out_json_path)
-
-
-if __name__ == "__main__":
-    fire.Fire(
-        {
-            "init_seed": init_seed,
-            "train": train,
-            "validate": validate,
-        }
-    )
diff --git a/examples/flower-client/client275-20240423-182644/fedn.yaml b/examples/flower-client/client275-20240423-182644/fedn.yaml
deleted file mode 100644
index 9fb33f18f..000000000
--- a/examples/flower-client/client275-20240423-182644/fedn.yaml
+++ /dev/null
@@ -1,8 +0,0 @@
-python_env: python_env.yaml
-entry_points:
-  build:
-    command: python entrypoint.py init_seed
-  train:
-    command: python entrypoint.py train
-  validate:
-    command: python entrypoint.py validate
\ No newline at end of file
diff --git a/examples/flower-client/client275-20240423-182644/flwr_client.py b/examples/flower-client/client275-20240423-182644/flwr_client.py
deleted file mode 100644
index 297df3ca7..000000000
--- a/examples/flower-client/client275-20240423-182644/flwr_client.py
+++ /dev/null
@@ -1,41 +0,0 @@
-"""Flower client code using the ClientApp abstraction.
-Code adapted from https://github.com/adap/flower/tree/main/examples/app-pytorch.
-"""
-
-from flwr.client import ClientApp, NumPyClient
-from flwr_task import (DEVICE, Net, get_weights, load_data, set_weights, test,
-                       train)
-
-
-# Define FlowerClient and client_fn
-class FlowerClient(NumPyClient):
-    def __init__(self, cid) -> None:
-        super().__init__()
-        self.net = Net().to(DEVICE)
-        self.trainloader, self.testloader = load_data(
-            partition_id=int(cid), num_clients=10
-        )
-
-    def get_parameters(self, config):
-        return [val.cpu().numpy() for _, val in self.net.state_dict().items()]
-
-    def fit(self, parameters, config):
-        set_weights(self.net, parameters)
-        train(self.net, self.trainloader, epochs=3)
-        return get_weights(self.net), len(self.trainloader.dataset), {}
-
-    def evaluate(self, parameters, config):
-        set_weights(self.net, parameters)
-        loss, accuracy = test(self.net, self.testloader)
-        return loss, len(self.testloader.dataset), {"accuracy": accuracy}
-
-
-def client_fn(cid: str):
-    """Create and return an instance of Flower `Client`."""
-    return FlowerClient(cid).to_client()
-
-
-# Flower ClientApp
-app = ClientApp(
-    client_fn=client_fn,
-)
diff --git a/examples/flower-client/client275-20240423-182644/flwr_task.py b/examples/flower-client/client275-20240423-182644/flwr_task.py
deleted file mode 100644
index dea53d9cc..000000000
--- a/examples/flower-client/client275-20240423-182644/flwr_task.py
+++ /dev/null
@@ -1,95 +0,0 @@
-"""Flower client code for helper functions.
-Code adapted from https://github.com/adap/flower/tree/main/examples/app-pytorch.
-"""
-
-from collections import OrderedDict
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from flwr_datasets import FederatedDataset
-from torch.utils.data import DataLoader
-from torchvision.transforms import Compose, Normalize, ToTensor
-from tqdm import tqdm
-
-DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-
-class Net(nn.Module):
-    """Model (simple CNN adapted from 'PyTorch: A 60 Minute Blitz')"""
-
-    def __init__(self) -> None:
-        super(Net, self).__init__()
-        self.conv1 = nn.Conv2d(3, 6, 5)
-        self.pool = nn.MaxPool2d(2, 2)
-        self.conv2 = nn.Conv2d(6, 16, 5)
-        self.fc1 = nn.Linear(16 * 5 * 5, 120)
-        self.fc2 = nn.Linear(120, 84)
-        self.fc3 = nn.Linear(84, 10)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.pool(F.relu(self.conv1(x)))
-        x = self.pool(F.relu(self.conv2(x)))
-        x = x.view(-1, 16 * 5 * 5)
-        x = F.relu(self.fc1(x))
-        x = F.relu(self.fc2(x))
-        return self.fc3(x)
-
-
-def load_data(partition_id, num_clients):
-    """Load partition CIFAR10 data."""
-    fds = FederatedDataset(dataset="cifar10", partitioners={"train": num_clients})
-    partition = fds.load_partition(partition_id)
-    # Divide data on each node: 80% train, 20% test
-    partition_train_test = partition.train_test_split(test_size=0.2)
-    pytorch_transforms = Compose(
-        [ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
-    )
-
-    def apply_transforms(batch):
-        """Apply transforms to the partition from FederatedDataset."""
-        batch["img"] = [pytorch_transforms(img) for img in batch["img"]]
-        return batch
-
-    partition_train_test = partition_train_test.with_transform(apply_transforms)
-    trainloader = DataLoader(partition_train_test["train"], batch_size=32, shuffle=True)
-    testloader = DataLoader(partition_train_test["test"], batch_size=32)
-    return trainloader, testloader
-
-
-def train(net, trainloader, epochs):
-    """Train the model on the training set."""
-    criterion = torch.nn.CrossEntropyLoss()
-    optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
-    for _ in range(epochs):
-        for batch in tqdm(trainloader, "Training"):
-            images = batch["img"]
-            labels = batch["label"]
-            optimizer.zero_grad()
-            criterion(net(images.to(DEVICE)), labels.to(DEVICE)).backward()
-            optimizer.step()
-
-
-def test(net, testloader):
-    """Validate the model on the test set."""
-    criterion = torch.nn.CrossEntropyLoss()
-    correct, loss = 0, 0.0
-    with torch.no_grad():
-        for batch in tqdm(testloader, "Testing"):
-            images = batch["img"].to(DEVICE)
-            labels = batch["label"].to(DEVICE)
-            outputs = net(images)
-            loss += criterion(outputs, labels).item()
-            correct += (torch.max(outputs.data, 1)[1] == labels).sum().item()
-    accuracy = correct / len(testloader.dataset)
-    return loss, accuracy
-
-
-def get_weights(net):
-    return [val.cpu().numpy() for _, val in net.state_dict().items()]
-
-
-def set_weights(net, parameters):
-    params_dict = zip(net.state_dict().keys(), parameters)
-    state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict})
-    net.load_state_dict(state_dict, strict=True)
diff --git a/examples/flower-client/client275-20240423-182644/python_env.yaml b/examples/flower-client/client275-20240423-182644/python_env.yaml
deleted file mode 100644
index 984a2e96d..000000000
--- a/examples/flower-client/client275-20240423-182644/python_env.yaml
+++ /dev/null
@@ -1,11 +0,0 @@
-name: flower-client
-build_dependencies:
-  - pip
-  - setuptools
-  - wheel==0.37.1
-dependencies:
-  - torch==2.2.1
-  - torchvision==0.17.1
-  - fire==0.3.1
-  - fedn[flower]==0.9.0
-  - flwr-datasets[vision]==0.1.0
\ No newline at end of file
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diff --git a/examples/flower-client/seed.npz b/examples/flower-client/seed.npz
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