diff --git a/e2e/bare-client-auth/client.py b/e2e/bare-client-auth/client.py
index e82f17088bd9..c7b0d59b8ea5 100644
--- a/e2e/bare-client-auth/client.py
+++ b/e2e/bare-client-auth/client.py
@@ -1,13 +1,14 @@
 import numpy as np
 
-import flwr as fl
+from flwr.client import ClientApp, NumPyClient
+from flwr.common import Context
 
 model_params = np.array([1])
 objective = 5
 
 
 # Define Flower client
-class FlowerClient(fl.client.NumPyClient):
+class FlowerClient(NumPyClient):
     def get_parameters(self, config):
         return model_params
 
@@ -23,10 +24,10 @@ def evaluate(self, parameters, config):
         return loss, 1, {"accuracy": accuracy}
 
 
-def client_fn(cid):
+def client_fn(context: Context):
     return FlowerClient().to_client()
 
 
-app = fl.client.ClientApp(
+app = ClientApp(
     client_fn=client_fn,
 )
diff --git a/e2e/bare-https/client.py b/e2e/bare-https/client.py
index 8f5c1412fd01..4a682af3aec3 100644
--- a/e2e/bare-https/client.py
+++ b/e2e/bare-https/client.py
@@ -2,14 +2,15 @@
 
 import numpy as np
 
-import flwr as fl
+from flwr.client import ClientApp, NumPyClient, start_client
+from flwr.common import Context
 
 model_params = np.array([1])
 objective = 5
 
 
 # Define Flower client
-class FlowerClient(fl.client.NumPyClient):
+class FlowerClient(NumPyClient):
     def get_parameters(self, config):
         return model_params
 
@@ -25,17 +26,17 @@ def evaluate(self, parameters, config):
         return loss, 1, {"accuracy": accuracy}
 
 
-def client_fn(cid):
+def client_fn(context: Context):
     return FlowerClient().to_client()
 
 
-app = fl.client.ClientApp(
+app = ClientApp(
     client_fn=client_fn,
 )
 
 if __name__ == "__main__":
     # Start Flower client
-    fl.client.start_client(
+    start_client(
         server_address="127.0.0.1:8080",
         client=FlowerClient().to_client(),
         root_certificates=Path("certificates/ca.crt").read_bytes(),
diff --git a/e2e/bare/client.py b/e2e/bare/client.py
index 402d775ac3a9..943e60d5db9f 100644
--- a/e2e/bare/client.py
+++ b/e2e/bare/client.py
@@ -2,8 +2,8 @@
 
 import numpy as np
 
-import flwr as fl
-from flwr.common import ConfigsRecord
+from flwr.client import ClientApp, NumPyClient, start_client
+from flwr.common import ConfigsRecord, Context
 
 SUBSET_SIZE = 1000
 STATE_VAR = "timestamp"
@@ -14,7 +14,7 @@
 
 
 # Define Flower client
-class FlowerClient(fl.client.NumPyClient):
+class FlowerClient(NumPyClient):
     def get_parameters(self, config):
         return model_params
 
@@ -51,16 +51,14 @@ def evaluate(self, parameters, config):
         )
 
 
-def client_fn(cid):
+def client_fn(context: Context):
     return FlowerClient().to_client()
 
 
-app = fl.client.ClientApp(
+app = ClientApp(
     client_fn=client_fn,
 )
 
 if __name__ == "__main__":
     # Start Flower client
-    fl.client.start_client(
-        server_address="127.0.0.1:8080", client=FlowerClient().to_client()
-    )
+    start_client(server_address="127.0.0.1:8080", client=FlowerClient().to_client())
diff --git a/e2e/docker/client.py b/e2e/docker/client.py
index 8451b810416b..44313c7c3af6 100644
--- a/e2e/docker/client.py
+++ b/e2e/docker/client.py
@@ -9,6 +9,7 @@
 from torchvision.transforms import Compose, Normalize, ToTensor
 
 from flwr.client import ClientApp, NumPyClient
+from flwr.common import Context
 
 # #############################################################################
 # 1. Regular PyTorch pipeline: nn.Module, train, test, and DataLoader
@@ -122,7 +123,7 @@ def evaluate(self, parameters, config):
         return loss, len(testloader.dataset), {"accuracy": accuracy}
 
 
-def client_fn(cid: str):
+def client_fn(context: Context):
     """Create and return an instance of Flower `Client`."""
     return FlowerClient().to_client()
 
diff --git a/e2e/framework-fastai/client.py b/e2e/framework-fastai/client.py
index 1d98a1134941..161b27b5a548 100644
--- a/e2e/framework-fastai/client.py
+++ b/e2e/framework-fastai/client.py
@@ -5,7 +5,8 @@
 import torch
 from fastai.vision.all import *
 
-import flwr as fl
+from flwr.client import ClientApp, NumPyClient, start_client
+from flwr.common import Context
 
 warnings.filterwarnings("ignore", category=UserWarning)
 
@@ -29,7 +30,7 @@
 
 
 # Define Flower client
-class FlowerClient(fl.client.NumPyClient):
+class FlowerClient(NumPyClient):
     def get_parameters(self, config):
         return [val.cpu().numpy() for _, val in learn.model.state_dict().items()]
 
@@ -49,18 +50,18 @@ def evaluate(self, parameters, config):
         return loss, len(dls.valid), {"accuracy": 1 - error_rate}
 
 
-def client_fn(cid):
+def client_fn(context: Context):
     return FlowerClient().to_client()
 
 
-app = fl.client.ClientApp(
+app = ClientApp(
     client_fn=client_fn,
 )
 
 
 if __name__ == "__main__":
     # Start Flower client
-    fl.client.start_client(
+    start_client(
         server_address="127.0.0.1:8080",
         client=FlowerClient().to_client(),
     )
diff --git a/e2e/framework-jax/client.py b/e2e/framework-jax/client.py
index 347a005d923a..c9ff67b3e38e 100644
--- a/e2e/framework-jax/client.py
+++ b/e2e/framework-jax/client.py
@@ -6,7 +6,8 @@
 import jax_training
 import numpy as np
 
-import flwr as fl
+from flwr.client import ClientApp, NumPyClient, start_client
+from flwr.common import Context
 
 # Load data and determine model shape
 train_x, train_y, test_x, test_y = jax_training.load_data()
@@ -14,7 +15,7 @@
 model_shape = train_x.shape[1:]
 
 
-class FlowerClient(fl.client.NumPyClient):
+class FlowerClient(NumPyClient):
     def __init__(self):
         self.params = jax_training.load_model(model_shape)
 
@@ -48,16 +49,14 @@ def evaluate(
         return float(loss), num_examples, {"loss": float(loss)}
 
 
-def client_fn(cid):
+def client_fn(context: Context):
     return FlowerClient().to_client()
 
 
-app = fl.client.ClientApp(
+app = ClientApp(
     client_fn=client_fn,
 )
 
 if __name__ == "__main__":
     # Start Flower client
-    fl.client.start_client(
-        server_address="127.0.0.1:8080", client=FlowerClient().to_client()
-    )
+    start_client(server_address="127.0.0.1:8080", client=FlowerClient().to_client())
diff --git a/e2e/framework-opacus/client.py b/e2e/framework-opacus/client.py
index c9ebe319063a..167fa4584e37 100644
--- a/e2e/framework-opacus/client.py
+++ b/e2e/framework-opacus/client.py
@@ -9,7 +9,8 @@
 from torch.utils.data import DataLoader
 from torchvision.datasets import CIFAR10
 
-import flwr as fl
+from flwr.client import ClientApp, NumPyClient, start_client
+from flwr.common import Context
 
 # Define parameters.
 PARAMS = {
@@ -95,7 +96,7 @@ def load_data():
 
 
 # Define Flower client.
-class FlowerClient(fl.client.NumPyClient):
+class FlowerClient(NumPyClient):
     def __init__(self, model) -> None:
         super().__init__()
         # Create a privacy engine which will add DP and keep track of the privacy budget.
@@ -139,16 +140,16 @@ def evaluate(self, parameters, config):
         return float(loss), len(testloader), {"accuracy": float(accuracy)}
 
 
-def client_fn(cid):
+def client_fn(context: Context):
     model = Net()
     return FlowerClient(model).to_client()
 
 
-app = fl.client.ClientApp(
+app = ClientApp(
     client_fn=client_fn,
 )
 
 if __name__ == "__main__":
-    fl.client.start_client(
+    start_client(
         server_address="127.0.0.1:8080", client=FlowerClient(model).to_client()
     )
diff --git a/e2e/framework-pandas/client.py b/e2e/framework-pandas/client.py
index 19e15f5a3b11..0c3300e1dd3f 100644
--- a/e2e/framework-pandas/client.py
+++ b/e2e/framework-pandas/client.py
@@ -3,7 +3,8 @@
 import numpy as np
 import pandas as pd
 
-import flwr as fl
+from flwr.client import ClientApp, NumPyClient, start_client
+from flwr.common import Context
 
 df = pd.read_csv("./data/client.csv")
 
@@ -16,7 +17,7 @@ def compute_hist(df: pd.DataFrame, col_name: str) -> np.ndarray:
 
 
 # Define Flower client
-class FlowerClient(fl.client.NumPyClient):
+class FlowerClient(NumPyClient):
     def fit(
         self, parameters: List[np.ndarray], config: Dict[str, str]
     ) -> Tuple[List[np.ndarray], int, Dict]:
@@ -32,17 +33,17 @@ def fit(
         )
 
 
-def client_fn(cid):
+def client_fn(context: Context):
     return FlowerClient().to_client()
 
 
-app = fl.client.ClientApp(
+app = ClientApp(
     client_fn=client_fn,
 )
 
 if __name__ == "__main__":
     # Start Flower client
-    fl.client.start_client(
+    start_client(
         server_address="127.0.0.1:8080",
         client=FlowerClient().to_client(),
     )
diff --git a/e2e/framework-pytorch-lightning/client.py b/e2e/framework-pytorch-lightning/client.py
index fdd55b3dc344..bf291a1ca2c5 100644
--- a/e2e/framework-pytorch-lightning/client.py
+++ b/e2e/framework-pytorch-lightning/client.py
@@ -4,10 +4,11 @@
 import pytorch_lightning as pl
 import torch
 
-import flwr as fl
+from flwr.client import ClientApp, NumPyClient, start_client
+from flwr.common import Context
 
 
-class FlowerClient(fl.client.NumPyClient):
+class FlowerClient(NumPyClient):
     def __init__(self, model, train_loader, val_loader, test_loader):
         self.model = model
         self.train_loader = train_loader
@@ -51,7 +52,7 @@ def _set_parameters(model, parameters):
     model.load_state_dict(state_dict, strict=True)
 
 
-def client_fn(cid):
+def client_fn(context: Context):
     model = mnist.LitAutoEncoder()
     train_loader, val_loader, test_loader = mnist.load_data()
 
@@ -59,7 +60,7 @@ def client_fn(cid):
     return FlowerClient(model, train_loader, val_loader, test_loader).to_client()
 
 
-app = fl.client.ClientApp(
+app = ClientApp(
     client_fn=client_fn,
 )
 
@@ -71,7 +72,7 @@ def main() -> None:
 
     # Flower client
     client = FlowerClient(model, train_loader, val_loader, test_loader).to_client()
-    fl.client.start_client(server_address="127.0.0.1:8080", client=client)
+    start_client(server_address="127.0.0.1:8080", client=client)
 
 
 if __name__ == "__main__":
diff --git a/e2e/framework-pytorch/client.py b/e2e/framework-pytorch/client.py
index dbfbfed1ffa7..ab4bc7b5c5b9 100644
--- a/e2e/framework-pytorch/client.py
+++ b/e2e/framework-pytorch/client.py
@@ -10,8 +10,8 @@
 from torchvision.transforms import Compose, Normalize, ToTensor
 from tqdm import tqdm
 
-import flwr as fl
-from flwr.common import ConfigsRecord
+from flwr.client import ClientApp, NumPyClient, start_client
+from flwr.common import ConfigsRecord, Context
 
 # #############################################################################
 # 1. Regular PyTorch pipeline: nn.Module, train, test, and DataLoader
@@ -89,7 +89,7 @@ def load_data():
 
 
 # Define Flower client
-class FlowerClient(fl.client.NumPyClient):
+class FlowerClient(NumPyClient):
     def get_parameters(self, config):
         return [val.cpu().numpy() for _, val in net.state_dict().items()]
 
@@ -136,18 +136,18 @@ def set_parameters(model, parameters):
     return
 
 
-def client_fn(cid):
+def client_fn(context: Context):
     return FlowerClient().to_client()
 
 
-app = fl.client.ClientApp(
+app = ClientApp(
     client_fn=client_fn,
 )
 
 
 if __name__ == "__main__":
     # Start Flower client
-    fl.client.start_client(
+    start_client(
         server_address="127.0.0.1:8080",
         client=FlowerClient().to_client(),
     )
diff --git a/e2e/framework-scikit-learn/client.py b/e2e/framework-scikit-learn/client.py
index b0691e75a79d..24c6617c1289 100644
--- a/e2e/framework-scikit-learn/client.py
+++ b/e2e/framework-scikit-learn/client.py
@@ -5,7 +5,8 @@
 from sklearn.linear_model import LogisticRegression
 from sklearn.metrics import log_loss
 
-import flwr as fl
+from flwr.client import ClientApp, NumPyClient, start_client
+from flwr.common import Context
 
 # Load MNIST dataset from https://www.openml.org/d/554
 (X_train, y_train), (X_test, y_test) = utils.load_mnist()
@@ -26,7 +27,7 @@
 
 
 # Define Flower client
-class FlowerClient(fl.client.NumPyClient):
+class FlowerClient(NumPyClient):
     def get_parameters(self, config):  # type: ignore
         return utils.get_model_parameters(model)
 
@@ -45,16 +46,14 @@ def evaluate(self, parameters, config):  # type: ignore
         return loss, len(X_test), {"accuracy": accuracy}
 
 
-def client_fn(cid):
+def client_fn(context: Context):
     return FlowerClient().to_client()
 
 
-app = fl.client.ClientApp(
+app = ClientApp(
     client_fn=client_fn,
 )
 
 if __name__ == "__main__":
     # Start Flower client
-    fl.client.start_client(
-        server_address="0.0.0.0:8080", client=FlowerClient().to_client()
-    )
+    start_client(server_address="0.0.0.0:8080", client=FlowerClient().to_client())
diff --git a/e2e/framework-tensorflow/client.py b/e2e/framework-tensorflow/client.py
index 779be0c3746d..351f495a3acb 100644
--- a/e2e/framework-tensorflow/client.py
+++ b/e2e/framework-tensorflow/client.py
@@ -2,7 +2,8 @@
 
 import tensorflow as tf
 
-import flwr as fl
+from flwr.client import ClientApp, NumPyClient, start_client
+from flwr.common import Context
 
 SUBSET_SIZE = 1000
 
@@ -18,7 +19,7 @@
 
 
 # Define Flower client
-class FlowerClient(fl.client.NumPyClient):
+class FlowerClient(NumPyClient):
     def get_parameters(self, config):
         return model.get_weights()
 
@@ -33,16 +34,14 @@ def evaluate(self, parameters, config):
         return loss, len(x_test), {"accuracy": accuracy}
 
 
-def client_fn(cid):
+def client_fn(context: Context):
     return FlowerClient().to_client()
 
 
-app = fl.client.ClientApp(
+app = ClientApp(
     client_fn=client_fn,
 )
 
 if __name__ == "__main__":
     # Start Flower client
-    fl.client.start_client(
-        server_address="127.0.0.1:8080", client=FlowerClient().to_client()
-    )
+    start_client(server_address="127.0.0.1:8080", client=FlowerClient().to_client())
diff --git a/e2e/strategies/client.py b/e2e/strategies/client.py
index 505340e013a5..0403416cc3b7 100644
--- a/e2e/strategies/client.py
+++ b/e2e/strategies/client.py
@@ -2,7 +2,8 @@
 
 import tensorflow as tf
 
-import flwr as fl
+from flwr.client import ClientApp, NumPyClient, start_client
+from flwr.common import Context
 
 SUBSET_SIZE = 1000
 
@@ -33,7 +34,7 @@ def get_model():
 
 
 # Define Flower client
-class FlowerClient(fl.client.NumPyClient):
+class FlowerClient(NumPyClient):
     def get_parameters(self, config):
         return model.get_weights()
 
@@ -48,17 +49,15 @@ def evaluate(self, parameters, config):
         return loss, len(x_test), {"accuracy": accuracy}
 
 
-def client_fn(cid):
+def client_fn(context: Context):
     return FlowerClient().to_client()
 
 
-app = fl.client.ClientApp(
+app = ClientApp(
     client_fn=client_fn,
 )
 
 
 if __name__ == "__main__":
     # Start Flower client
-    fl.client.start_client(
-        server_address="127.0.0.1:8080", client=FlowerClient().to_client()
-    )
+    start_client(server_address="127.0.0.1:8080", client=FlowerClient().to_client())
diff --git a/e2e/strategies/test.py b/e2e/strategies/test.py
index abf9cdb5a5c7..c567f33b236b 100644
--- a/e2e/strategies/test.py
+++ b/e2e/strategies/test.py
@@ -3,8 +3,8 @@
 import tensorflow as tf
 from client import SUBSET_SIZE, FlowerClient, get_model
 
-import flwr as fl
-from flwr.common import ndarrays_to_parameters
+from flwr.common import Context, ndarrays_to_parameters
+from flwr.server import ServerConfig
 from flwr.server.strategy import (
     FaultTolerantFedAvg,
     FedAdagrad,
@@ -15,6 +15,7 @@
     FedYogi,
     QFedAvg,
 )
+from flwr.simulation import start_simulation
 
 STRATEGY_LIST = [
     FedMedian,
@@ -42,8 +43,7 @@ def get_strat(name):
 init_model = get_model()
 
 
-def client_fn(cid):
-    _ = cid
+def client_fn(context: Context):
     return FlowerClient()
 
 
@@ -71,10 +71,10 @@ def evaluate(server_round, parameters, config):
 if start_idx >= OPT_IDX:
     strat_args["tau"] = 0.01
 
-hist = fl.simulation.start_simulation(
+hist = start_simulation(
     client_fn=client_fn,
     num_clients=2,
-    config=fl.server.ServerConfig(num_rounds=3),
+    config=ServerConfig(num_rounds=3),
     strategy=strategy(**strat_args),
 )