diff --git a/src/py/flwr/cli/new/new.py b/src/py/flwr/cli/new/new.py
index 797df12b715..3cbde991ff6 100644
--- a/src/py/flwr/cli/new/new.py
+++ b/src/py/flwr/cli/new/new.py
@@ -240,6 +240,7 @@ def new(
             MlFramework.HUGGINGFACE.value,
             MlFramework.MLX.value,
             MlFramework.TENSORFLOW.value,
+            MlFramework.SKLEARN.value,
             MlFramework.NUMPY.value,
         ]
         if framework_str in frameworks_with_tasks:
diff --git a/src/py/flwr/cli/new/templates/app/code/client.sklearn.py.tpl b/src/py/flwr/cli/new/templates/app/code/client.sklearn.py.tpl
index 2d3d1c7f163..69d208ac28c 100644
--- a/src/py/flwr/cli/new/templates/app/code/client.sklearn.py.tpl
+++ b/src/py/flwr/cli/new/templates/app/code/client.sklearn.py.tpl
@@ -2,40 +2,17 @@
 
 import warnings
 
-import numpy as np
-from flwr.client import NumPyClient, ClientApp
-from flwr.common import Context
-from flwr_datasets import FederatedDataset
-from sklearn.linear_model import LogisticRegression
 from sklearn.metrics import log_loss
 
-
-def get_model_parameters(model):
-    if model.fit_intercept:
-        params = [
-            model.coef_,
-            model.intercept_,
-        ]
-    else:
-        params = [model.coef_]
-    return params
-
-
-def set_model_params(model, params):
-    model.coef_ = params[0]
-    if model.fit_intercept:
-        model.intercept_ = params[1]
-    return model
-
-
-def set_initial_params(model):
-    n_classes = 10  # MNIST has 10 classes
-    n_features = 784  # Number of features in dataset
-    model.classes_ = np.array([i for i in range(10)])
-
-    model.coef_ = np.zeros((n_classes, n_features))
-    if model.fit_intercept:
-        model.intercept_ = np.zeros((n_classes,))
+from flwr.client import ClientApp, NumPyClient
+from flwr.common import Context
+from $import_name.task import (
+    get_model,
+    get_model_params,
+    load_data,
+    set_initial_params,
+    set_model_params,
+)
 
 
 class FlowerClient(NumPyClient):
@@ -46,9 +23,6 @@ class FlowerClient(NumPyClient):
         self.y_train = y_train
         self.y_test = y_test
 
-    def get_parameters(self, config):
-        return get_model_parameters(self.model)
-
     def fit(self, parameters, config):
         set_model_params(self.model, parameters)
 
@@ -57,7 +31,7 @@ class FlowerClient(NumPyClient):
             warnings.simplefilter("ignore")
             self.model.fit(self.X_train, self.y_train)
 
-        return get_model_parameters(self.model), len(self.X_train), {}
+        return get_model_params(self.model), len(self.X_train), {}
 
     def evaluate(self, parameters, config):
         set_model_params(self.model, parameters)
@@ -71,21 +45,13 @@ class FlowerClient(NumPyClient):
 def client_fn(context: Context):
     partition_id = context.node_config["partition-id"]
     num_partitions = context.node_config["num-partitions"]
-    fds = FederatedDataset(dataset="mnist", partitioners={"train": num_partitions})
-    dataset = fds.load_partition(partition_id, "train").with_format("numpy")
-
-    X, y = dataset["image"].reshape((len(dataset), -1)), dataset["label"]
 
-    # Split the on edge data: 80% train, 20% test
-    X_train, X_test = X[: int(0.8 * len(X))], X[int(0.8 * len(X)) :]
-    y_train, y_test = y[: int(0.8 * len(y))], y[int(0.8 * len(y)) :]
+    X_train, X_test, y_train, y_test = load_data(partition_id, num_partitions)
 
     # Create LogisticRegression Model
-    model = LogisticRegression(
-        penalty="l2",
-        max_iter=1,  # local epoch
-        warm_start=True,  # prevent refreshing weights when fitting
-    )
+    penalty = context.run_config["penalty"]
+    local_epochs = context.run_config["local-epochs"]
+    model = get_model(penalty, local_epochs)
 
     # Setting initial parameters, akin to model.compile for keras models
     set_initial_params(model)
diff --git a/src/py/flwr/cli/new/templates/app/code/server.sklearn.py.tpl b/src/py/flwr/cli/new/templates/app/code/server.sklearn.py.tpl
index 678ba932622..b1487b01d2d 100644
--- a/src/py/flwr/cli/new/templates/app/code/server.sklearn.py.tpl
+++ b/src/py/flwr/cli/new/templates/app/code/server.sklearn.py.tpl
@@ -1,19 +1,31 @@
 """$project_name: A Flower / $framework_str app."""
 
-from flwr.common import Context
+from flwr.common import Context, ndarrays_to_parameters
 from flwr.server import ServerApp, ServerAppComponents, ServerConfig
 from flwr.server.strategy import FedAvg
+from $import_name.task import get_model, get_model_params, set_initial_params
 
 
 def server_fn(context: Context):
     # Read from config
     num_rounds = context.run_config["num-server-rounds"]
 
+    # Create LogisticRegression Model
+    penalty = context.run_config["penalty"]
+    local_epochs = context.run_config["local-epochs"]
+    model = get_model(penalty, local_epochs)
+
+    # Setting initial parameters, akin to model.compile for keras models
+    set_initial_params(model)
+
+    initial_parameters = ndarrays_to_parameters(get_model_params(model))
+
     # Define strategy
     strategy = FedAvg(
         fraction_fit=1.0,
         fraction_evaluate=1.0,
         min_available_clients=2,
+        initial_parameters=initial_parameters,
     )
     config = ServerConfig(num_rounds=num_rounds)
 
diff --git a/src/py/flwr/cli/new/templates/app/code/task.sklearn.py.tpl b/src/py/flwr/cli/new/templates/app/code/task.sklearn.py.tpl
new file mode 100644
index 00000000000..52c13edc032
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/code/task.sklearn.py.tpl
@@ -0,0 +1,67 @@
+"""$project_name: A Flower / $framework_str app."""
+
+import numpy as np
+from flwr_datasets import FederatedDataset
+from flwr_datasets.partitioner import IidPartitioner
+from sklearn.linear_model import LogisticRegression
+
+fds = None  # Cache FederatedDataset
+
+
+def load_data(partition_id: int, num_partitions: int):
+    """Load partition MNIST data."""
+    # Only initialize `FederatedDataset` once
+    global fds
+    if fds is None:
+        partitioner = IidPartitioner(num_partitions=num_partitions)
+        fds = FederatedDataset(
+            dataset="mnist",
+            partitioners={"train": partitioner},
+        )
+
+    dataset = fds.load_partition(partition_id, "train").with_format("numpy")
+
+    X, y = dataset["image"].reshape((len(dataset), -1)), dataset["label"]
+
+    # Split the on edge data: 80% train, 20% test
+    X_train, X_test = X[: int(0.8 * len(X))], X[int(0.8 * len(X)) :]
+    y_train, y_test = y[: int(0.8 * len(y))], y[int(0.8 * len(y)) :]
+
+    return X_train, X_test, y_train, y_test
+
+
+def get_model(penalty: str, local_epochs: int):
+
+    return LogisticRegression(
+        penalty=penalty,
+        max_iter=local_epochs,
+        warm_start=True,
+    )
+
+
+def get_model_params(model):
+    if model.fit_intercept:
+        params = [
+            model.coef_,
+            model.intercept_,
+        ]
+    else:
+        params = [model.coef_]
+    return params
+
+
+def set_model_params(model, params):
+    model.coef_ = params[0]
+    if model.fit_intercept:
+        model.intercept_ = params[1]
+    return model
+
+
+def set_initial_params(model):
+    n_classes = 10  # MNIST has 10 classes
+    n_features = 784  # Number of features in dataset
+    model.classes_ = np.array([i for i in range(10)])
+
+    model.coef_ = np.zeros((n_classes, n_features))
+    if model.fit_intercept:
+        model.intercept_ = np.zeros((n_classes,))
diff --git a/src/py/flwr/cli/new/templates/app/pyproject.sklearn.toml.tpl b/src/py/flwr/cli/new/templates/app/pyproject.sklearn.toml.tpl
index 2b5778fec9a..c351531fda0 100644
--- a/src/py/flwr/cli/new/templates/app/pyproject.sklearn.toml.tpl
+++ b/src/py/flwr/cli/new/templates/app/pyproject.sklearn.toml.tpl
@@ -25,6 +25,8 @@ clientapp = "$import_name.client_app:app"
 
 [tool.flwr.app.config]
 num-server-rounds = 3
+penalty = "l2"
+local-epochs = 1
 
 [tool.flwr.federations]
 default = "local-simulation"