From 7ea2a4f88dd140cea439fd72c8d3757d5b2119d6 Mon Sep 17 00:00:00 2001
From: Mohammad Naseri <mohammad@flower.ai>
Date: Tue, 12 Mar 2024 14:26:29 +0000
Subject: [PATCH 01/31] Add dp secagg demo example

---
 examples/dp-secagg-demo/client.py          | 51 ++++++++++++
 examples/dp-secagg-demo/server_workflow.py | 63 +++++++++++++++
 examples/dp-secagg-demo/task.py            | 94 ++++++++++++++++++++++
 3 files changed, 208 insertions(+)
 create mode 100644 examples/dp-secagg-demo/client.py
 create mode 100644 examples/dp-secagg-demo/server_workflow.py
 create mode 100644 examples/dp-secagg-demo/task.py

diff --git a/examples/dp-secagg-demo/client.py b/examples/dp-secagg-demo/client.py
new file mode 100644
index 000000000000..ebbe977ecab1
--- /dev/null
+++ b/examples/dp-secagg-demo/client.py
@@ -0,0 +1,51 @@
+from flwr.client import ClientApp, NumPyClient
+
+from task import (
+    Net,
+    DEVICE,
+    load_data,
+    get_weights,
+    set_weights,
+    train,
+    test,
+)
+
+
+# Load model and data (simple CNN, CIFAR-10)
+net = Net().to(DEVICE)
+trainloader, testloader = load_data()
+
+
+# Define FlowerClient and client_fn
+class FlowerClient(NumPyClient):
+
+    def fit(self, parameters, config):
+        set_weights(net, parameters)
+        results = train(net, trainloader, testloader, epochs=1, device=DEVICE)
+        return get_weights(net), len(trainloader.dataset), results
+
+    def evaluate(self, parameters, config):
+        set_weights(net, parameters)
+        loss, accuracy = test(net, testloader)
+        return loss, len(testloader.dataset), {"accuracy": accuracy}
+
+
+def client_fn(cid: str):
+    """Create and return an instance of Flower `Client`."""
+    return FlowerClient().to_client()
+
+
+# Flower ClientApp
+app = ClientApp(
+    client_fn=client_fn,
+)
+
+
+# Legacy mode
+if __name__ == "__main__":
+    from flwr.client import start_client
+
+    start_client(
+        server_address="127.0.0.1:8080",
+        client=FlowerClient().to_client(),
+    )
diff --git a/examples/dp-secagg-demo/server_workflow.py b/examples/dp-secagg-demo/server_workflow.py
new file mode 100644
index 000000000000..6923010ecf7b
--- /dev/null
+++ b/examples/dp-secagg-demo/server_workflow.py
@@ -0,0 +1,63 @@
+from typing import List, Tuple
+
+from task import Net, get_weights
+
+import flwr as fl
+from flwr.common import Context, Metrics, ndarrays_to_parameters
+from flwr.server import Driver, LegacyContext
+
+
+# Define metric aggregation function
+def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
+    examples = [num_examples for num_examples, _ in metrics]
+
+    # Multiply accuracy of each client by number of examples used
+    train_losses = [num_examples * m["train_loss"] for num_examples, m in metrics]
+    train_accuracies = [
+        num_examples * m["train_accuracy"] for num_examples, m in metrics
+    ]
+    val_losses = [num_examples * m["val_loss"] for num_examples, m in metrics]
+    val_accuracies = [num_examples * m["val_accuracy"] for num_examples, m in metrics]
+
+    # Aggregate and return custom metric (weighted average)
+    return {
+        "train_loss": sum(train_losses) / sum(examples),
+        "train_accuracy": sum(train_accuracies) / sum(examples),
+        "val_loss": sum(val_losses) / sum(examples),
+        "val_accuracy": sum(val_accuracies) / sum(examples),
+    }
+
+
+# Initialize model parameters
+ndarrays = get_weights(Net())
+parameters = ndarrays_to_parameters(ndarrays)
+
+
+# Define strategy
+strategy = fl.server.strategy.FedAvg(
+    fraction_fit=1.0,  # Select all available clients
+    fraction_evaluate=0.0,  # Disable evaluation
+    min_available_clients=2,
+    fit_metrics_aggregation_fn=weighted_average,
+    initial_parameters=parameters,
+)
+
+
+# Run via `flower-server-app server_workflow:app`
+app = fl.server.ServerApp()
+
+
+@app.main()
+def main(driver: Driver, context: Context) -> None:
+    # Construct the LegacyContext
+    context = LegacyContext(
+        state=context.state,
+        config=fl.server.ServerConfig(num_rounds=3),
+        strategy=strategy,
+    )
+
+    # Create the workflow
+    workflow = fl.server.workflow.DefaultWorkflow()
+
+    # Execute
+    workflow(driver, context)
diff --git a/examples/dp-secagg-demo/task.py b/examples/dp-secagg-demo/task.py
new file mode 100644
index 000000000000..240f290df320
--- /dev/null
+++ b/examples/dp-secagg-demo/task.py
@@ -0,0 +1,94 @@
+from collections import OrderedDict
+from logging import INFO
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from flwr.common.logger import log
+from torch.utils.data import DataLoader
+from torchvision.datasets import CIFAR10
+from torchvision.transforms import Compose, Normalize, ToTensor
+
+
+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():
+    """Load CIFAR-10 (training and test set)."""
+    trf = Compose([ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+    trainset = CIFAR10("./data", train=True, download=True, transform=trf)
+    testset = CIFAR10("./data", train=False, download=True, transform=trf)
+    return DataLoader(trainset, batch_size=32, shuffle=True), DataLoader(testset)
+
+
+def train(net, trainloader, valloader, epochs, device):
+    """Train the model on the training set."""
+    log(INFO, "Starting training...")
+    net.to(device)  # move model to GPU if available
+    criterion = torch.nn.CrossEntropyLoss().to(device)
+    optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
+    net.train()
+    for _ in range(epochs):
+        for images, labels in trainloader:
+            images, labels = images.to(device), labels.to(device)
+            optimizer.zero_grad()
+            loss = criterion(net(images), labels)
+            loss.backward()
+            optimizer.step()
+
+    train_loss, train_acc = test(net, trainloader)
+    val_loss, val_acc = test(net, valloader)
+
+    results = {
+        "train_loss": train_loss,
+        "train_accuracy": train_acc,
+        "val_loss": val_loss,
+        "val_accuracy": val_acc,
+    }
+    return results
+
+
+def test(net, testloader):
+    """Validate the model on the test set."""
+    net.to(DEVICE)
+    criterion = torch.nn.CrossEntropyLoss()
+    correct, loss = 0, 0.0
+    with torch.no_grad():
+        for images, labels in testloader:
+            outputs = net(images.to(DEVICE))
+            labels = labels.to(DEVICE)
+            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)

From 3f32370aa5cc5430eea81a1d91245baab49d8f81 Mon Sep 17 00:00:00 2001
From: Heng Pan <pan@flower.ai>
Date: Tue, 12 Mar 2024 18:13:52 +0000
Subject: [PATCH 02/31] add sa

---
 examples/dp-secagg-demo/client.py             | 27 +++++--------------
 .../{server_workflow.py => server.py}         | 12 ++++++---
 2 files changed, 15 insertions(+), 24 deletions(-)
 rename examples/dp-secagg-demo/{server_workflow.py => server.py} (84%)

diff --git a/examples/dp-secagg-demo/client.py b/examples/dp-secagg-demo/client.py
index ebbe977ecab1..ed69e6edebbc 100644
--- a/examples/dp-secagg-demo/client.py
+++ b/examples/dp-secagg-demo/client.py
@@ -1,15 +1,7 @@
-from flwr.client import ClientApp, NumPyClient
-
-from task import (
-    Net,
-    DEVICE,
-    load_data,
-    get_weights,
-    set_weights,
-    train,
-    test,
-)
+from task import DEVICE, Net, get_weights, load_data, set_weights, test, train
 
+from flwr.client import ClientApp, NumPyClient
+from flwr.client.mod import secaggplus_mod
 
 # Load model and data (simple CNN, CIFAR-10)
 net = Net().to(DEVICE)
@@ -38,14 +30,7 @@ def client_fn(cid: str):
 # Flower ClientApp
 app = ClientApp(
     client_fn=client_fn,
+    mods=[
+        secaggplus_mod,
+    ],
 )
-
-
-# Legacy mode
-if __name__ == "__main__":
-    from flwr.client import start_client
-
-    start_client(
-        server_address="127.0.0.1:8080",
-        client=FlowerClient().to_client(),
-    )
diff --git a/examples/dp-secagg-demo/server_workflow.py b/examples/dp-secagg-demo/server.py
similarity index 84%
rename from examples/dp-secagg-demo/server_workflow.py
rename to examples/dp-secagg-demo/server.py
index 6923010ecf7b..dd4be383cf7f 100644
--- a/examples/dp-secagg-demo/server_workflow.py
+++ b/examples/dp-secagg-demo/server.py
@@ -5,6 +5,7 @@
 import flwr as fl
 from flwr.common import Context, Metrics, ndarrays_to_parameters
 from flwr.server import Driver, LegacyContext
+from flwr.server.workflow import SecAggPlusWorkflow
 
 
 # Define metric aggregation function
@@ -35,9 +36,9 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
 
 # Define strategy
 strategy = fl.server.strategy.FedAvg(
-    fraction_fit=1.0,  # Select all available clients
+    fraction_fit=0.2,  # Select 20% of all available clients
     fraction_evaluate=0.0,  # Disable evaluation
-    min_available_clients=2,
+    min_available_clients=100,
     fit_metrics_aggregation_fn=weighted_average,
     initial_parameters=parameters,
 )
@@ -57,7 +58,12 @@ def main(driver: Driver, context: Context) -> None:
     )
 
     # Create the workflow
-    workflow = fl.server.workflow.DefaultWorkflow()
+    workflow = fl.server.workflow.DefaultWorkflow(
+        fit_workflow=SecAggPlusWorkflow(
+            num_shares=7,
+            reconstruction_threshold=4,
+        )
+    )
 
     # Execute
     workflow(driver, context)

From 1f1762777e37eb4e887b0dd96450e131b53ad1f0 Mon Sep 17 00:00:00 2001
From: Mohammad Naseri <mohammad@flower.ai>
Date: Wed, 13 Mar 2024 15:38:00 +0000
Subject: [PATCH 03/31] Add clientside fixed clipping

---
 examples/dp-secagg-demo/client.py |  3 +++
 examples/dp-secagg-demo/server.py | 14 +++++++++-----
 2 files changed, 12 insertions(+), 5 deletions(-)

diff --git a/examples/dp-secagg-demo/client.py b/examples/dp-secagg-demo/client.py
index ed69e6edebbc..2810d366984f 100644
--- a/examples/dp-secagg-demo/client.py
+++ b/examples/dp-secagg-demo/client.py
@@ -3,6 +3,8 @@
 from flwr.client import ClientApp, NumPyClient
 from flwr.client.mod import secaggplus_mod
 
+from flwr.client.mod.centraldp_mods import fixedclipping_mod
+
 # Load model and data (simple CNN, CIFAR-10)
 net = Net().to(DEVICE)
 trainloader, testloader = load_data()
@@ -32,5 +34,6 @@ def client_fn(cid: str):
     client_fn=client_fn,
     mods=[
         secaggplus_mod,
+        fixedclipping_mod,
     ],
 )
diff --git a/examples/dp-secagg-demo/server.py b/examples/dp-secagg-demo/server.py
index dd4be383cf7f..d57861549b1f 100644
--- a/examples/dp-secagg-demo/server.py
+++ b/examples/dp-secagg-demo/server.py
@@ -6,6 +6,7 @@
 from flwr.common import Context, Metrics, ndarrays_to_parameters
 from flwr.server import Driver, LegacyContext
 from flwr.server.workflow import SecAggPlusWorkflow
+from flwr.server.strategy.dp_fixed_clipping import DifferentialPrivacyClientSideFixedClipping
 
 
 # Define metric aggregation function
@@ -36,13 +37,16 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
 
 # Define strategy
 strategy = fl.server.strategy.FedAvg(
-    fraction_fit=0.2,  # Select 20% of all available clients
+    fraction_fit=1.0,  # Select 20% of all available clients
     fraction_evaluate=0.0,  # Disable evaluation
-    min_available_clients=100,
+    min_fit_clients=3,
+    min_available_clients=3,
     fit_metrics_aggregation_fn=weighted_average,
     initial_parameters=parameters,
 )
 
+dp_strategy = DifferentialPrivacyClientSideFixedClipping(strategy, 0.5, 10, 3)
+
 
 # Run via `flower-server-app server_workflow:app`
 app = fl.server.ServerApp()
@@ -54,14 +58,14 @@ def main(driver: Driver, context: Context) -> None:
     context = LegacyContext(
         state=context.state,
         config=fl.server.ServerConfig(num_rounds=3),
-        strategy=strategy,
+        strategy=dp_strategy,
     )
 
     # Create the workflow
     workflow = fl.server.workflow.DefaultWorkflow(
         fit_workflow=SecAggPlusWorkflow(
-            num_shares=7,
-            reconstruction_threshold=4,
+            num_shares=3,
+            reconstruction_threshold=2,
         )
     )
 

From 8674db0f207c0bdf7b2d6f048a584ba48ab18090 Mon Sep 17 00:00:00 2001
From: Mohammad Naseri <mohammad@flower.ai>
Date: Wed, 13 Mar 2024 15:56:20 +0000
Subject: [PATCH 04/31] Fix

---
 examples/dp-secagg-demo/client.py |  2 +-
 examples/dp-secagg-demo/server.py | 12 ++++++------
 examples/dp-secagg-demo/task.py   |  6 +++++-
 3 files changed, 12 insertions(+), 8 deletions(-)

diff --git a/examples/dp-secagg-demo/client.py b/examples/dp-secagg-demo/client.py
index 2810d366984f..1f8bf0fe6db0 100644
--- a/examples/dp-secagg-demo/client.py
+++ b/examples/dp-secagg-demo/client.py
@@ -15,7 +15,7 @@ class FlowerClient(NumPyClient):
 
     def fit(self, parameters, config):
         set_weights(net, parameters)
-        results = train(net, trainloader, testloader, epochs=1, device=DEVICE)
+        results = train(net, trainloader, testloader, iters=10, epochs=1, device=DEVICE)
         return get_weights(net), len(trainloader.dataset), results
 
     def evaluate(self, parameters, config):
diff --git a/examples/dp-secagg-demo/server.py b/examples/dp-secagg-demo/server.py
index d57861549b1f..acf20df87cf1 100644
--- a/examples/dp-secagg-demo/server.py
+++ b/examples/dp-secagg-demo/server.py
@@ -37,15 +37,15 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
 
 # Define strategy
 strategy = fl.server.strategy.FedAvg(
-    fraction_fit=1.0,  # Select 20% of all available clients
+    fraction_fit=0.2,  # Select 20% of all available clients
     fraction_evaluate=0.0,  # Disable evaluation
-    min_fit_clients=3,
-    min_available_clients=3,
+    min_fit_clients=20,
+    min_available_clients=100,
     fit_metrics_aggregation_fn=weighted_average,
     initial_parameters=parameters,
 )
 
-dp_strategy = DifferentialPrivacyClientSideFixedClipping(strategy, 0.5, 10, 3)
+dp_strategy = DifferentialPrivacyClientSideFixedClipping(strategy, 0.5, 10, 20)
 
 
 # Run via `flower-server-app server_workflow:app`
@@ -64,8 +64,8 @@ def main(driver: Driver, context: Context) -> None:
     # Create the workflow
     workflow = fl.server.workflow.DefaultWorkflow(
         fit_workflow=SecAggPlusWorkflow(
-            num_shares=3,
-            reconstruction_threshold=2,
+            num_shares=7,
+            reconstruction_threshold=4,
         )
     )
 
diff --git a/examples/dp-secagg-demo/task.py b/examples/dp-secagg-demo/task.py
index 240f290df320..157c1be12a0f 100644
--- a/examples/dp-secagg-demo/task.py
+++ b/examples/dp-secagg-demo/task.py
@@ -42,13 +42,14 @@ def load_data():
     return DataLoader(trainset, batch_size=32, shuffle=True), DataLoader(testset)
 
 
-def train(net, trainloader, valloader, epochs, device):
+def train(net, trainloader, valloader, epochs, iters, device):
     """Train the model on the training set."""
     log(INFO, "Starting training...")
     net.to(device)  # move model to GPU if available
     criterion = torch.nn.CrossEntropyLoss().to(device)
     optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
     net.train()
+    iter_cnt = 0
     for _ in range(epochs):
         for images, labels in trainloader:
             images, labels = images.to(device), labels.to(device)
@@ -56,6 +57,9 @@ def train(net, trainloader, valloader, epochs, device):
             loss = criterion(net(images), labels)
             loss.backward()
             optimizer.step()
+            # iter_cnt += 1
+            # if iter_cnt >= iters:
+            #     return
 
     train_loss, train_acc = test(net, trainloader)
     val_loss, val_acc = test(net, valloader)

From c38fc907454d330f4bc45b6f244b40a4db94dac7 Mon Sep 17 00:00:00 2001
From: Heng Pan <pan@flower.ai>
Date: Wed, 13 Mar 2024 18:01:53 +0000
Subject: [PATCH 05/31] update

---
 examples/dp-secagg-demo/client.py | 16 ++++++++++------
 examples/dp-secagg-demo/server.py |  8 ++++----
 examples/dp-secagg-demo/task.py   | 32 ++++++++++++++++++++-----------
 3 files changed, 35 insertions(+), 21 deletions(-)

diff --git a/examples/dp-secagg-demo/client.py b/examples/dp-secagg-demo/client.py
index 1f8bf0fe6db0..d901fb8d849b 100644
--- a/examples/dp-secagg-demo/client.py
+++ b/examples/dp-secagg-demo/client.py
@@ -7,26 +7,30 @@
 
 # Load model and data (simple CNN, CIFAR-10)
 net = Net().to(DEVICE)
-trainloader, testloader = load_data()
 
 
 # Define FlowerClient and client_fn
 class FlowerClient(NumPyClient):
+    
+    def __init__(self, trainloader, testloader) -> None:
+        self.trainloader = trainloader
+        self.testloader = testloader
 
     def fit(self, parameters, config):
         set_weights(net, parameters)
-        results = train(net, trainloader, testloader, iters=10, epochs=1, device=DEVICE)
-        return get_weights(net), len(trainloader.dataset), results
+        results = train(net, self.trainloader, self.testloader, epochs=1, device=DEVICE)
+        return get_weights(net), len(self.trainloader.dataset), results
 
     def evaluate(self, parameters, config):
         set_weights(net, parameters)
-        loss, accuracy = test(net, testloader)
-        return loss, len(testloader.dataset), {"accuracy": accuracy}
+        loss, accuracy = test(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().to_client()
+    trainloader, testloader = load_data(partition_id=int(cid))
+    return FlowerClient(trainloader, testloader).to_client()
 
 
 # Flower ClientApp
diff --git a/examples/dp-secagg-demo/server.py b/examples/dp-secagg-demo/server.py
index acf20df87cf1..e9ebc3f7f982 100644
--- a/examples/dp-secagg-demo/server.py
+++ b/examples/dp-secagg-demo/server.py
@@ -37,15 +37,15 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
 
 # Define strategy
 strategy = fl.server.strategy.FedAvg(
-    fraction_fit=0.2,  # Select 20% of all available clients
+    fraction_fit=1.0,  # Select 20% of all available clients
     fraction_evaluate=0.0,  # Disable evaluation
-    min_fit_clients=20,
-    min_available_clients=100,
+    min_fit_clients=10,
+    min_available_clients=10,
     fit_metrics_aggregation_fn=weighted_average,
     initial_parameters=parameters,
 )
 
-dp_strategy = DifferentialPrivacyClientSideFixedClipping(strategy, 0.5, 10, 20)
+dp_strategy = DifferentialPrivacyClientSideFixedClipping(strategy, 0.5, 10, 10)
 
 
 # Run via `flower-server-app server_workflow:app`
diff --git a/examples/dp-secagg-demo/task.py b/examples/dp-secagg-demo/task.py
index 157c1be12a0f..2154d20de730 100644
--- a/examples/dp-secagg-demo/task.py
+++ b/examples/dp-secagg-demo/task.py
@@ -1,5 +1,6 @@
 from collections import OrderedDict
 from logging import INFO
+from flwr_datasets import FederatedDataset
 
 import torch
 import torch.nn as nn
@@ -34,22 +35,34 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         return self.fc3(x)
 
 
-def load_data():
-    """Load CIFAR-10 (training and test set)."""
-    trf = Compose([ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
-    trainset = CIFAR10("./data", train=True, download=True, transform=trf)
-    testset = CIFAR10("./data", train=False, download=True, transform=trf)
-    return DataLoader(trainset, batch_size=32, shuffle=True), DataLoader(testset)
+def load_data(partition_id):
+    """Load partition CIFAR10 data."""
+    fds = FederatedDataset(dataset="cifar10", partitioners={"train": 100})
+    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
 
-def train(net, trainloader, valloader, epochs, iters, device):
+    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, valloader, epochs, device):
     """Train the model on the training set."""
     log(INFO, "Starting training...")
     net.to(device)  # move model to GPU if available
     criterion = torch.nn.CrossEntropyLoss().to(device)
     optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
     net.train()
-    iter_cnt = 0
     for _ in range(epochs):
         for images, labels in trainloader:
             images, labels = images.to(device), labels.to(device)
@@ -57,9 +70,6 @@ def train(net, trainloader, valloader, epochs, iters, device):
             loss = criterion(net(images), labels)
             loss.backward()
             optimizer.step()
-            # iter_cnt += 1
-            # if iter_cnt >= iters:
-            #     return
 
     train_loss, train_acc = test(net, trainloader)
     val_loss, val_acc = test(net, valloader)

From 55739f889f8f9931a41879de65bc7e620245e226 Mon Sep 17 00:00:00 2001
From: Heng Pan <pan@flower.ai>
Date: Wed, 13 Mar 2024 18:56:07 +0000
Subject: [PATCH 06/31] fix a bug causing data loading failure

---
 examples/dp-secagg-demo/client.py |  2 +-
 examples/dp-secagg-demo/server.py | 12 +++++++-----
 examples/dp-secagg-demo/task.py   |  9 ++++++---
 3 files changed, 14 insertions(+), 9 deletions(-)

diff --git a/examples/dp-secagg-demo/client.py b/examples/dp-secagg-demo/client.py
index d901fb8d849b..a1a3b1467215 100644
--- a/examples/dp-secagg-demo/client.py
+++ b/examples/dp-secagg-demo/client.py
@@ -11,7 +11,7 @@
 
 # Define FlowerClient and client_fn
 class FlowerClient(NumPyClient):
-    
+
     def __init__(self, trainloader, testloader) -> None:
         self.trainloader = trainloader
         self.testloader = testloader
diff --git a/examples/dp-secagg-demo/server.py b/examples/dp-secagg-demo/server.py
index e9ebc3f7f982..54ea27c10cc5 100644
--- a/examples/dp-secagg-demo/server.py
+++ b/examples/dp-secagg-demo/server.py
@@ -6,7 +6,9 @@
 from flwr.common import Context, Metrics, ndarrays_to_parameters
 from flwr.server import Driver, LegacyContext
 from flwr.server.workflow import SecAggPlusWorkflow
-from flwr.server.strategy.dp_fixed_clipping import DifferentialPrivacyClientSideFixedClipping
+from flwr.server.strategy.dp_fixed_clipping import (
+    DifferentialPrivacyClientSideFixedClipping,
+)
 
 
 # Define metric aggregation function
@@ -37,15 +39,15 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
 
 # Define strategy
 strategy = fl.server.strategy.FedAvg(
-    fraction_fit=1.0,  # Select 20% of all available clients
+    fraction_fit=0.2,  # Select 20% of all available clients
     fraction_evaluate=0.0,  # Disable evaluation
-    min_fit_clients=10,
-    min_available_clients=10,
+    min_fit_clients=20,
+    min_available_clients=100,
     fit_metrics_aggregation_fn=weighted_average,
     initial_parameters=parameters,
 )
 
-dp_strategy = DifferentialPrivacyClientSideFixedClipping(strategy, 0.5, 10, 10)
+dp_strategy = DifferentialPrivacyClientSideFixedClipping(strategy, 0.5, 10, 20)
 
 
 # Run via `flower-server-app server_workflow:app`
diff --git a/examples/dp-secagg-demo/task.py b/examples/dp-secagg-demo/task.py
index 2154d20de730..2fe68260c3f5 100644
--- a/examples/dp-secagg-demo/task.py
+++ b/examples/dp-secagg-demo/task.py
@@ -64,8 +64,9 @@ def train(net, trainloader, valloader, epochs, device):
     optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
     net.train()
     for _ in range(epochs):
-        for images, labels in trainloader:
-            images, labels = images.to(device), labels.to(device)
+        for batch in trainloader:
+            images = batch["img"].to(device)
+            labels = batch["label"].to(device)
             optimizer.zero_grad()
             loss = criterion(net(images), labels)
             loss.backward()
@@ -89,7 +90,9 @@ def test(net, testloader):
     criterion = torch.nn.CrossEntropyLoss()
     correct, loss = 0, 0.0
     with torch.no_grad():
-        for images, labels in testloader:
+        for batch in testloader:
+            images = batch["img"].to(DEVICE)
+            labels = batch["label"].to(DEVICE)
             outputs = net(images.to(DEVICE))
             labels = labels.to(DEVICE)
             loss += criterion(outputs, labels).item()

From 0d3d0237f1470f39f3669ca4b1503dc2abf047a4 Mon Sep 17 00:00:00 2001
From: Heng Pan <pan@flower.ai>
Date: Wed, 13 Mar 2024 20:57:08 +0000
Subject: [PATCH 07/31] update configs

---
 examples/dp-secagg-demo/server.py | 8 ++++----
 examples/dp-secagg-demo/task.py   | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/examples/dp-secagg-demo/server.py b/examples/dp-secagg-demo/server.py
index 54ea27c10cc5..1682cb2da86c 100644
--- a/examples/dp-secagg-demo/server.py
+++ b/examples/dp-secagg-demo/server.py
@@ -39,15 +39,15 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
 
 # Define strategy
 strategy = fl.server.strategy.FedAvg(
-    fraction_fit=0.2,  # Select 20% of all available clients
+    fraction_fit=1.0,  # Select 20% of all available clients
     fraction_evaluate=0.0,  # Disable evaluation
-    min_fit_clients=20,
-    min_available_clients=100,
+    min_fit_clients=40,
+    min_available_clients=40,
     fit_metrics_aggregation_fn=weighted_average,
     initial_parameters=parameters,
 )
 
-dp_strategy = DifferentialPrivacyClientSideFixedClipping(strategy, 0.5, 10, 20)
+dp_strategy = DifferentialPrivacyClientSideFixedClipping(strategy, 0.1, 10, 40)
 
 
 # Run via `flower-server-app server_workflow:app`
diff --git a/examples/dp-secagg-demo/task.py b/examples/dp-secagg-demo/task.py
index 2fe68260c3f5..0743c9587b8b 100644
--- a/examples/dp-secagg-demo/task.py
+++ b/examples/dp-secagg-demo/task.py
@@ -37,7 +37,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 def load_data(partition_id):
     """Load partition CIFAR10 data."""
-    fds = FederatedDataset(dataset="cifar10", partitioners={"train": 100})
+    fds = FederatedDataset(dataset="cifar10", partitioners={"train": 40})
     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)

From 503212fc1938fcdfccf649ee3c276d592fa24c7c Mon Sep 17 00:00:00 2001
From: mohammadnaseri <mohammad@flower.dev>
Date: Wed, 13 Mar 2024 21:40:13 +0000
Subject: [PATCH 08/31] Create README.md

---
 examples/dp-secagg-demo/README.md | 3 +++
 1 file changed, 3 insertions(+)
 create mode 100644 examples/dp-secagg-demo/README.md

diff --git a/examples/dp-secagg-demo/README.md b/examples/dp-secagg-demo/README.md
new file mode 100644
index 000000000000..807cc392f60f
--- /dev/null
+++ b/examples/dp-secagg-demo/README.md
@@ -0,0 +1,3 @@
+# DP + SecAgg Demo Example
+This is a simple example which utilizes central differential privacy with client-side fixed clipping with secure aggregation. 
+Note: It is for small number of rounds and just for demo purpose. 

From 13acf884310199f389cf4b5f66751d92e9f5fb9e Mon Sep 17 00:00:00 2001
From: mohammadnaseri <mohammad@flower.dev>
Date: Wed, 13 Mar 2024 21:56:41 +0000
Subject: [PATCH 09/31] Update README.md

---
 examples/dp-secagg-demo/README.md | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/examples/dp-secagg-demo/README.md b/examples/dp-secagg-demo/README.md
index 807cc392f60f..872259dcf4c0 100644
--- a/examples/dp-secagg-demo/README.md
+++ b/examples/dp-secagg-demo/README.md
@@ -1,3 +1,4 @@
 # DP + SecAgg Demo Example
-This is a simple example which utilizes central differential privacy with client-side fixed clipping with secure aggregation. 
-Note: It is for small number of rounds and just for demo purpose. 
+
+This is a simple example which utilizes central differential privacy with client-side fixed clipping with secure aggregation.
+Note: It is for small number of rounds and just for demo purpose.

From 9d1eadf72f75e94ecd3c1286ed094a525fc7b6da Mon Sep 17 00:00:00 2001
From: Mohammad Naseri <mohammad@flower.ai>
Date: Wed, 13 Mar 2024 22:22:24 +0000
Subject: [PATCH 10/31] Update

---
 examples/dp-secagg-demo/server.py | 13 ++++++++-----
 examples/dp-secagg-demo/task.py   |  2 +-
 2 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/examples/dp-secagg-demo/server.py b/examples/dp-secagg-demo/server.py
index 1682cb2da86c..c960b9bd6b91 100644
--- a/examples/dp-secagg-demo/server.py
+++ b/examples/dp-secagg-demo/server.py
@@ -39,15 +39,18 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
 
 # Define strategy
 strategy = fl.server.strategy.FedAvg(
-    fraction_fit=1.0,  # Select 20% of all available clients
+    fraction_fit=0.2,
     fraction_evaluate=0.0,  # Disable evaluation
-    min_fit_clients=40,
-    min_available_clients=40,
+    min_fit_clients=20,
+    min_available_clients=20,
     fit_metrics_aggregation_fn=weighted_average,
     initial_parameters=parameters,
 )
 
-dp_strategy = DifferentialPrivacyClientSideFixedClipping(strategy, 0.1, 10, 40)
+dp_strategy = DifferentialPrivacyClientSideFixedClipping(strategy, 
+                                                         noise_multiplier=0.1, 
+                                                         clipping_norm=10,
+                                                         num_sampled_clients=20)
 
 
 # Run via `flower-server-app server_workflow:app`
@@ -59,7 +62,7 @@ def main(driver: Driver, context: Context) -> None:
     # Construct the LegacyContext
     context = LegacyContext(
         state=context.state,
-        config=fl.server.ServerConfig(num_rounds=3),
+        config=fl.server.ServerConfig(num_rounds=10),
         strategy=dp_strategy,
     )
 
diff --git a/examples/dp-secagg-demo/task.py b/examples/dp-secagg-demo/task.py
index 0743c9587b8b..2fe68260c3f5 100644
--- a/examples/dp-secagg-demo/task.py
+++ b/examples/dp-secagg-demo/task.py
@@ -37,7 +37,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 def load_data(partition_id):
     """Load partition CIFAR10 data."""
-    fds = FederatedDataset(dataset="cifar10", partitioners={"train": 40})
+    fds = FederatedDataset(dataset="cifar10", partitioners={"train": 100})
     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)

From 62a23481164aa859956606bfcd0cc25f8a346152 Mon Sep 17 00:00:00 2001
From: mohammadnaseri <mohammad@flower.dev>
Date: Wed, 13 Mar 2024 22:31:35 +0000
Subject: [PATCH 11/31] Update README.md

---
 examples/dp-secagg-demo/README.md | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/examples/dp-secagg-demo/README.md b/examples/dp-secagg-demo/README.md
index 872259dcf4c0..ffb3032fff8c 100644
--- a/examples/dp-secagg-demo/README.md
+++ b/examples/dp-secagg-demo/README.md
@@ -1,4 +1,11 @@
 # DP + SecAgg Demo Example
 
-This is a simple example which utilizes central differential privacy with client-side fixed clipping with secure aggregation.
-Note: It is for small number of rounds and just for demo purpose.
+This is a simple example that utilizes central differential privacy with client-side fixed clipping and secure aggregation.
+Note: This example is designed for a small number of rounds and is intended for demonstration purposes.
+
+## Run
+
+The example uses the CIFAR-10 dataset with a total of 100 clients, with 20 clients sampled in each round. The hyperparameters for DP and SecAgg are specified in `server.py`.
+
+```shell
+flower-simulation --server-app server:app --client-app client:app --num-supernodes 100

From 6986158b745c3a58ec197a5ac4bedb5320b943c9 Mon Sep 17 00:00:00 2001
From: Mohammad Naseri <mohammad@flower.ai>
Date: Wed, 13 Mar 2024 16:52:48 +0000
Subject: [PATCH 12/31] Add logging to central dp wrappers

---
 .../server/strategy/dp_adaptive_clipping.py   | 19 ++++++++++++++++++-
 .../flwr/server/strategy/dp_fixed_clipping.py | 19 ++++++++++++++++++-
 2 files changed, 36 insertions(+), 2 deletions(-)

diff --git a/src/py/flwr/server/strategy/dp_adaptive_clipping.py b/src/py/flwr/server/strategy/dp_adaptive_clipping.py
index d9422c791167..8627f16c5588 100644
--- a/src/py/flwr/server/strategy/dp_adaptive_clipping.py
+++ b/src/py/flwr/server/strategy/dp_adaptive_clipping.py
@@ -19,7 +19,7 @@
 
 
 import math
-from logging import WARNING
+from logging import DEBUG, INFO, WARNING
 from typing import Dict, List, Optional, Tuple, Union
 
 import numpy as np
@@ -39,6 +39,7 @@
     adaptive_clip_inputs_inplace,
     add_gaussian_noise_to_params,
     compute_adaptive_noise_params,
+    compute_stdv,
 )
 from flwr.common.differential_privacy_constants import (
     CLIENTS_DISCREPANCY_WARNING,
@@ -225,6 +226,14 @@ def aggregate_fit(
                 self.clipping_norm,
                 self.num_sampled_clients,
             )
+            log(INFO, "aggregate_fit: central dp noise added to params.")
+            log(
+                DEBUG,
+                "aggregate_fit: central dp noise with standard deviation: %s added to params.",
+                compute_stdv(
+                    self.noise_multiplier, self.clipping_norm, self.num_sampled_clients
+                ),
+            )
 
         return aggregated_params, metrics
 
@@ -408,6 +417,14 @@ def aggregate_fit(
                 self.clipping_norm,
                 self.num_sampled_clients,
             )
+            log(INFO, "aggregate_fit: central dp noise added to params.")
+            log(
+                DEBUG,
+                "aggregate_fit: central dp noise with standard deviation: %s added to params.",
+                compute_stdv(
+                    self.noise_multiplier, self.clipping_norm, self.num_sampled_clients
+                ),
+            )
 
         return aggregated_params, metrics
 
diff --git a/src/py/flwr/server/strategy/dp_fixed_clipping.py b/src/py/flwr/server/strategy/dp_fixed_clipping.py
index 69930ce49c0b..d0a4701baf2f 100644
--- a/src/py/flwr/server/strategy/dp_fixed_clipping.py
+++ b/src/py/flwr/server/strategy/dp_fixed_clipping.py
@@ -18,7 +18,7 @@
 """
 
 
-from logging import WARNING
+from logging import DEBUG, INFO, WARNING
 from typing import Dict, List, Optional, Tuple, Union
 
 from flwr.common import (
@@ -35,6 +35,7 @@
 from flwr.common.differential_privacy import (
     add_gaussian_noise_to_params,
     compute_clip_model_update,
+    compute_stdv,
 )
 from flwr.common.differential_privacy_constants import (
     CLIENTS_DISCREPANCY_WARNING,
@@ -171,6 +172,14 @@ def aggregate_fit(
                 self.clipping_norm,
                 self.num_sampled_clients,
             )
+            log(INFO, "aggregate_fit: central dp noise added to params.")
+            log(
+                DEBUG,
+                "aggregate_fit: central dp noise with standard deviation: %s added to params.",
+                compute_stdv(
+                    self.noise_multiplier, self.clipping_norm, self.num_sampled_clients
+                ),
+            )
 
         return aggregated_params, metrics
 
@@ -321,6 +330,14 @@ def aggregate_fit(
                 self.clipping_norm,
                 self.num_sampled_clients,
             )
+            log(INFO, "aggregate_fit: central dp noise added to params.")
+            log(
+                DEBUG,
+                "aggregate_fit: central dp noise with standard deviation: %s added to params.",
+                compute_stdv(
+                    self.noise_multiplier, self.clipping_norm, self.num_sampled_clients
+                ),
+            )
         return aggregated_params, metrics
 
     def aggregate_evaluate(

From ddd3f47a14c399b047fa7ff7c6830e669bb32417 Mon Sep 17 00:00:00 2001
From: Mohammad Naseri <mohammad@flower.ai>
Date: Wed, 13 Mar 2024 19:33:37 +0000
Subject: [PATCH 13/31] Update

---
 src/py/flwr/server/strategy/dp_adaptive_clipping.py | 8 +++-----
 src/py/flwr/server/strategy/dp_fixed_clipping.py    | 9 ++++-----
 2 files changed, 7 insertions(+), 10 deletions(-)

diff --git a/src/py/flwr/server/strategy/dp_adaptive_clipping.py b/src/py/flwr/server/strategy/dp_adaptive_clipping.py
index 8627f16c5588..ec74ce0a94a0 100644
--- a/src/py/flwr/server/strategy/dp_adaptive_clipping.py
+++ b/src/py/flwr/server/strategy/dp_adaptive_clipping.py
@@ -19,7 +19,7 @@
 
 
 import math
-from logging import DEBUG, INFO, WARNING
+from logging import INFO, WARNING
 from typing import Dict, List, Optional, Tuple, Union
 
 import numpy as np
@@ -226,9 +226,8 @@ def aggregate_fit(
                 self.clipping_norm,
                 self.num_sampled_clients,
             )
-            log(INFO, "aggregate_fit: central dp noise added to params.")
             log(
-                DEBUG,
+                INFO,
                 "aggregate_fit: central dp noise with standard deviation: %s added to params.",
                 compute_stdv(
                     self.noise_multiplier, self.clipping_norm, self.num_sampled_clients
@@ -417,9 +416,8 @@ def aggregate_fit(
                 self.clipping_norm,
                 self.num_sampled_clients,
             )
-            log(INFO, "aggregate_fit: central dp noise added to params.")
             log(
-                DEBUG,
+                INFO,
                 "aggregate_fit: central dp noise with standard deviation: %s added to params.",
                 compute_stdv(
                     self.noise_multiplier, self.clipping_norm, self.num_sampled_clients
diff --git a/src/py/flwr/server/strategy/dp_fixed_clipping.py b/src/py/flwr/server/strategy/dp_fixed_clipping.py
index d0a4701baf2f..b1f3691e27db 100644
--- a/src/py/flwr/server/strategy/dp_fixed_clipping.py
+++ b/src/py/flwr/server/strategy/dp_fixed_clipping.py
@@ -18,7 +18,7 @@
 """
 
 
-from logging import DEBUG, INFO, WARNING
+from logging import INFO, WARNING
 from typing import Dict, List, Optional, Tuple, Union
 
 from flwr.common import (
@@ -172,9 +172,9 @@ def aggregate_fit(
                 self.clipping_norm,
                 self.num_sampled_clients,
             )
-            log(INFO, "aggregate_fit: central dp noise added to params.")
+
             log(
-                DEBUG,
+                INFO,
                 "aggregate_fit: central dp noise with standard deviation: %s added to params.",
                 compute_stdv(
                     self.noise_multiplier, self.clipping_norm, self.num_sampled_clients
@@ -330,9 +330,8 @@ def aggregate_fit(
                 self.clipping_norm,
                 self.num_sampled_clients,
             )
-            log(INFO, "aggregate_fit: central dp noise added to params.")
             log(
-                DEBUG,
+                INFO,
                 "aggregate_fit: central dp noise with standard deviation: %s added to params.",
                 compute_stdv(
                     self.noise_multiplier, self.clipping_norm, self.num_sampled_clients

From f3b122b4f4e56027cbc77b775afd198d733bc64f Mon Sep 17 00:00:00 2001
From: Mohammad Naseri <mohammad@flower.ai>
Date: Wed, 13 Mar 2024 20:30:55 +0000
Subject: [PATCH 14/31] Update

---
 src/py/flwr/server/strategy/dp_adaptive_clipping.py | 4 ++--
 src/py/flwr/server/strategy/dp_fixed_clipping.py    | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/py/flwr/server/strategy/dp_adaptive_clipping.py b/src/py/flwr/server/strategy/dp_adaptive_clipping.py
index ec74ce0a94a0..9a1116ea4458 100644
--- a/src/py/flwr/server/strategy/dp_adaptive_clipping.py
+++ b/src/py/flwr/server/strategy/dp_adaptive_clipping.py
@@ -228,7 +228,7 @@ def aggregate_fit(
             )
             log(
                 INFO,
-                "aggregate_fit: central dp noise with standard deviation: %s added to params.",
+                "aggregate_fit: central DP noise with standard deviation: %s added to parameters.",
                 compute_stdv(
                     self.noise_multiplier, self.clipping_norm, self.num_sampled_clients
                 ),
@@ -418,7 +418,7 @@ def aggregate_fit(
             )
             log(
                 INFO,
-                "aggregate_fit: central dp noise with standard deviation: %s added to params.",
+                "aggregate_fit: central DP noise with standard deviation: %s added to parameters.",
                 compute_stdv(
                     self.noise_multiplier, self.clipping_norm, self.num_sampled_clients
                 ),
diff --git a/src/py/flwr/server/strategy/dp_fixed_clipping.py b/src/py/flwr/server/strategy/dp_fixed_clipping.py
index b1f3691e27db..6714841e2fec 100644
--- a/src/py/flwr/server/strategy/dp_fixed_clipping.py
+++ b/src/py/flwr/server/strategy/dp_fixed_clipping.py
@@ -175,7 +175,7 @@ def aggregate_fit(
 
             log(
                 INFO,
-                "aggregate_fit: central dp noise with standard deviation: %s added to params.",
+                "aggregate_fit: central DP noise with standard deviation: %s added to parameters.",
                 compute_stdv(
                     self.noise_multiplier, self.clipping_norm, self.num_sampled_clients
                 ),
@@ -332,7 +332,7 @@ def aggregate_fit(
             )
             log(
                 INFO,
-                "aggregate_fit: central dp noise with standard deviation: %s added to params.",
+                "aggregate_fit: central DP noise with standard deviation: %s added to parameters.",
                 compute_stdv(
                     self.noise_multiplier, self.clipping_norm, self.num_sampled_clients
                 ),

From 5af8720fb829360a9c06b25f8dbad777f1dcdf7a Mon Sep 17 00:00:00 2001
From: Mohammad Naseri <mohammad@flower.ai>
Date: Wed, 13 Mar 2024 20:44:38 +0000
Subject: [PATCH 15/31] Update

---
 src/py/flwr/client/mod/centraldp_mods.py            | 5 +++++
 src/py/flwr/server/strategy/dp_adaptive_clipping.py | 6 ++++++
 src/py/flwr/server/strategy/dp_fixed_clipping.py    | 5 +++++
 3 files changed, 16 insertions(+)

diff --git a/src/py/flwr/client/mod/centraldp_mods.py b/src/py/flwr/client/mod/centraldp_mods.py
index 0c0134e0f876..6a3816ff0a0c 100644
--- a/src/py/flwr/client/mod/centraldp_mods.py
+++ b/src/py/flwr/client/mod/centraldp_mods.py
@@ -15,6 +15,8 @@
 """Clipping modifiers for central DP with client-side clipping."""
 
 
+from logging import INFO
+
 from flwr.client.typing import ClientAppCallable
 from flwr.common import ndarrays_to_parameters, parameters_to_ndarrays
 from flwr.common import recordset_compat as compat
@@ -25,6 +27,7 @@
     compute_clip_model_update,
 )
 from flwr.common.differential_privacy_constants import KEY_CLIPPING_NORM, KEY_NORM_BIT
+from flwr.common.logger import log
 from flwr.common.message import Message
 
 
@@ -79,6 +82,8 @@ def fixedclipping_mod(
         clipping_norm,
     )
 
+    log(INFO, "fixedclipping_mod: parameters are clipped by value: $s.", clipping_norm)
+
     fit_res.parameters = ndarrays_to_parameters(client_to_server_params)
     out_msg.content = compat.fitres_to_recordset(fit_res, keep_input=True)
     return out_msg
diff --git a/src/py/flwr/server/strategy/dp_adaptive_clipping.py b/src/py/flwr/server/strategy/dp_adaptive_clipping.py
index 9a1116ea4458..9facace42bf1 100644
--- a/src/py/flwr/server/strategy/dp_adaptive_clipping.py
+++ b/src/py/flwr/server/strategy/dp_adaptive_clipping.py
@@ -198,6 +198,12 @@ def aggregate_fit(
             norm_bit = adaptive_clip_inputs_inplace(model_update, self.clipping_norm)
             norm_bit_set_count += norm_bit
 
+            log(
+                INFO,
+                "aggregate_fit: parameters are clipped by value: $s.",
+                self.clipping_norm,
+            )
+
             for i, _ in enumerate(self.current_round_params):
                 param[i] = self.current_round_params[i] + model_update[i]
             # Convert back to parameters
diff --git a/src/py/flwr/server/strategy/dp_fixed_clipping.py b/src/py/flwr/server/strategy/dp_fixed_clipping.py
index 6714841e2fec..b4066a54746f 100644
--- a/src/py/flwr/server/strategy/dp_fixed_clipping.py
+++ b/src/py/flwr/server/strategy/dp_fixed_clipping.py
@@ -156,6 +156,11 @@ def aggregate_fit(
             compute_clip_model_update(
                 param, self.current_round_params, self.clipping_norm
             )
+            log(
+                INFO,
+                "aggregate_fit: parameters are clipped by value: $s.",
+                self.clipping_norm,
+            )
             # Convert back to parameters
             res.parameters = ndarrays_to_parameters(param)
 

From 6369c8ed105b4480a703795efc29863e49f73ead Mon Sep 17 00:00:00 2001
From: Mohammad Naseri <mohammad@flower.ai>
Date: Wed, 13 Mar 2024 20:50:49 +0000
Subject: [PATCH 16/31] Update

---
 src/py/flwr/client/mod/centraldp_mods.py |  5 +++++
 src/py/flwr/client/mod/localdp_mod.py    | 14 ++++++++++++++
 2 files changed, 19 insertions(+)

diff --git a/src/py/flwr/client/mod/centraldp_mods.py b/src/py/flwr/client/mod/centraldp_mods.py
index 6a3816ff0a0c..2aed8354a6cb 100644
--- a/src/py/flwr/client/mod/centraldp_mods.py
+++ b/src/py/flwr/client/mod/centraldp_mods.py
@@ -144,6 +144,11 @@ def adaptiveclipping_mod(
         server_to_client_params,
         clipping_norm,
     )
+    log(
+        INFO,
+        "adaptiveclipping_mod: parameters are clipped by value: $s.",
+        clipping_norm,
+    )
 
     fit_res.parameters = ndarrays_to_parameters(client_to_server_params)
 
diff --git a/src/py/flwr/client/mod/localdp_mod.py b/src/py/flwr/client/mod/localdp_mod.py
index 5f62c9e44800..e41ca9cc1544 100644
--- a/src/py/flwr/client/mod/localdp_mod.py
+++ b/src/py/flwr/client/mod/localdp_mod.py
@@ -15,6 +15,10 @@
 """Local DP modifier."""
 
 
+from logging import INFO
+
+import numpy as np
+
 from flwr.client.typing import ClientAppCallable
 from flwr.common import ndarrays_to_parameters, parameters_to_ndarrays
 from flwr.common import recordset_compat as compat
@@ -24,6 +28,7 @@
     add_localdp_gaussian_noise_to_params,
     compute_clip_model_update,
 )
+from flwr.common.logger import log
 from flwr.common.message import Message
 
 
@@ -122,6 +127,9 @@ def __call__(
             server_to_client_params,
             self.clipping_norm,
         )
+        log(
+            INFO, "LocalDpMod: parameters are clipped by value: $s.", self.clipping_norm
+        )
 
         fit_res.parameters = ndarrays_to_parameters(client_to_server_params)
 
@@ -129,6 +137,12 @@ def __call__(
         add_localdp_gaussian_noise_to_params(
             fit_res.parameters, self.sensitivity, self.epsilon, self.delta
         )
+        log(
+            INFO,
+            "LocalDpMod: local DP noise with "
+            "standard deviation: %s added to parameters.",
+            self.sensitivity * np.sqrt(2 * np.log(1.25 / self.delta)) / self.epsilon,
+        )
 
         out_msg.content = compat.fitres_to_recordset(fit_res, keep_input=True)
         return out_msg

From b0fa4203dc55c0be458b5d099bf4d4a31da5d669 Mon Sep 17 00:00:00 2001
From: Mohammad Naseri <mohammad@flower.ai>
Date: Wed, 13 Mar 2024 21:03:53 +0000
Subject: [PATCH 17/31] Update

---
 src/py/flwr/client/mod/centraldp_mods.py | 4 ++--
 src/py/flwr/client/mod/localdp_mod.py    | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/py/flwr/client/mod/centraldp_mods.py b/src/py/flwr/client/mod/centraldp_mods.py
index 2aed8354a6cb..4f4a595e8d9c 100644
--- a/src/py/flwr/client/mod/centraldp_mods.py
+++ b/src/py/flwr/client/mod/centraldp_mods.py
@@ -82,7 +82,7 @@ def fixedclipping_mod(
         clipping_norm,
     )
 
-    log(INFO, "fixedclipping_mod: parameters are clipped by value: $s.", clipping_norm)
+    log(INFO, "fixedclipping_mod: parameters are clipped by value: %s.", clipping_norm)
 
     fit_res.parameters = ndarrays_to_parameters(client_to_server_params)
     out_msg.content = compat.fitres_to_recordset(fit_res, keep_input=True)
@@ -146,7 +146,7 @@ def adaptiveclipping_mod(
     )
     log(
         INFO,
-        "adaptiveclipping_mod: parameters are clipped by value: $s.",
+        "adaptiveclipping_mod: parameters are clipped by value: %s.",
         clipping_norm,
     )
 
diff --git a/src/py/flwr/client/mod/localdp_mod.py b/src/py/flwr/client/mod/localdp_mod.py
index e41ca9cc1544..3b0311a612b9 100644
--- a/src/py/flwr/client/mod/localdp_mod.py
+++ b/src/py/flwr/client/mod/localdp_mod.py
@@ -128,7 +128,7 @@ def __call__(
             self.clipping_norm,
         )
         log(
-            INFO, "LocalDpMod: parameters are clipped by value: $s.", self.clipping_norm
+            INFO, "LocalDpMod: parameters are clipped by value: %s.", self.clipping_norm
         )
 
         fit_res.parameters = ndarrays_to_parameters(client_to_server_params)

From 920075433f1b8fc5f29a3781d880c7e35fec6531 Mon Sep 17 00:00:00 2001
From: Mohammad Naseri <mohammad@flower.ai>
Date: Wed, 13 Mar 2024 21:06:03 +0000
Subject: [PATCH 18/31] Update

---
 src/py/flwr/server/strategy/dp_adaptive_clipping.py | 2 +-
 src/py/flwr/server/strategy/dp_fixed_clipping.py    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/py/flwr/server/strategy/dp_adaptive_clipping.py b/src/py/flwr/server/strategy/dp_adaptive_clipping.py
index 9facace42bf1..c3c3761f9dc7 100644
--- a/src/py/flwr/server/strategy/dp_adaptive_clipping.py
+++ b/src/py/flwr/server/strategy/dp_adaptive_clipping.py
@@ -200,7 +200,7 @@ def aggregate_fit(
 
             log(
                 INFO,
-                "aggregate_fit: parameters are clipped by value: $s.",
+                "aggregate_fit: parameters are clipped by value: %s.",
                 self.clipping_norm,
             )
 
diff --git a/src/py/flwr/server/strategy/dp_fixed_clipping.py b/src/py/flwr/server/strategy/dp_fixed_clipping.py
index b4066a54746f..c670c26e4977 100644
--- a/src/py/flwr/server/strategy/dp_fixed_clipping.py
+++ b/src/py/flwr/server/strategy/dp_fixed_clipping.py
@@ -158,7 +158,7 @@ def aggregate_fit(
             )
             log(
                 INFO,
-                "aggregate_fit: parameters are clipped by value: $s.",
+                "aggregate_fit: parameters are clipped by value: %s.",
                 self.clipping_norm,
             )
             # Convert back to parameters

From 4dfdbd1686d67377602e6b16244f7ea9de68eb71 Mon Sep 17 00:00:00 2001
From: Mohammad Naseri <mohammad@flower.ai>
Date: Wed, 13 Mar 2024 22:33:20 +0000
Subject: [PATCH 19/31] Update

---
 examples/dp-secagg-demo/server.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/dp-secagg-demo/server.py b/examples/dp-secagg-demo/server.py
index c960b9bd6b91..90ac79b382d8 100644
--- a/examples/dp-secagg-demo/server.py
+++ b/examples/dp-secagg-demo/server.py
@@ -40,7 +40,7 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
 # Define strategy
 strategy = fl.server.strategy.FedAvg(
     fraction_fit=0.2,
-    fraction_evaluate=0.0,  # Disable evaluation
+    fraction_evaluate=0.0,  # Disable evaluation for demo purpose
     min_fit_clients=20,
     min_available_clients=20,
     fit_metrics_aggregation_fn=weighted_average,

From 50b785d572eefd187c37d1ae1a338332aef12f22 Mon Sep 17 00:00:00 2001
From: Mohammad Naseri <mohammad@flower.ai>
Date: Wed, 13 Mar 2024 23:13:04 +0000
Subject: [PATCH 20/31] Update

---
 examples/dp-secagg-demo/client.py | 1 -
 examples/dp-secagg-demo/server.py | 7 +++----
 2 files changed, 3 insertions(+), 5 deletions(-)

diff --git a/examples/dp-secagg-demo/client.py b/examples/dp-secagg-demo/client.py
index a1a3b1467215..6a6114b5e16c 100644
--- a/examples/dp-secagg-demo/client.py
+++ b/examples/dp-secagg-demo/client.py
@@ -11,7 +11,6 @@
 
 # Define FlowerClient and client_fn
 class FlowerClient(NumPyClient):
-
     def __init__(self, trainloader, testloader) -> None:
         self.trainloader = trainloader
         self.testloader = testloader
diff --git a/examples/dp-secagg-demo/server.py b/examples/dp-secagg-demo/server.py
index 90ac79b382d8..ff7d81b7ecd4 100644
--- a/examples/dp-secagg-demo/server.py
+++ b/examples/dp-secagg-demo/server.py
@@ -47,10 +47,9 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
     initial_parameters=parameters,
 )
 
-dp_strategy = DifferentialPrivacyClientSideFixedClipping(strategy, 
-                                                         noise_multiplier=0.1, 
-                                                         clipping_norm=10,
-                                                         num_sampled_clients=20)
+dp_strategy = DifferentialPrivacyClientSideFixedClipping(
+    strategy, noise_multiplier=0.1, clipping_norm=10, num_sampled_clients=20
+)
 
 
 # Run via `flower-server-app server_workflow:app`

From 578d6b56350a11b1c2076bbcb500df0cdb7ad997 Mon Sep 17 00:00:00 2001
From: mohammadnaseri <mohammad@flower.dev>
Date: Wed, 13 Mar 2024 23:23:35 +0000
Subject: [PATCH 21/31] Update README.md

---
 examples/dp-secagg-demo/README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/examples/dp-secagg-demo/README.md b/examples/dp-secagg-demo/README.md
index ffb3032fff8c..d5d5069222a7 100644
--- a/examples/dp-secagg-demo/README.md
+++ b/examples/dp-secagg-demo/README.md
@@ -9,3 +9,4 @@ The example uses the CIFAR-10 dataset with a total of 100 clients, with 20 clien
 
 ```shell
 flower-simulation --server-app server:app --client-app client:app --num-supernodes 100
+```

From b9273c711c052949ed49efda909555bf05d5e253 Mon Sep 17 00:00:00 2001
From: Heng Pan <pan@flower.ai>
Date: Thu, 14 Mar 2024 09:11:59 +0000
Subject: [PATCH 22/31] use MNIST and Adam

---
 examples/dp-secagg-demo/server.py |  2 +-
 examples/dp-secagg-demo/task.py   | 22 ++++++++++------------
 2 files changed, 11 insertions(+), 13 deletions(-)

diff --git a/examples/dp-secagg-demo/server.py b/examples/dp-secagg-demo/server.py
index ff7d81b7ecd4..47411b7b62fe 100644
--- a/examples/dp-secagg-demo/server.py
+++ b/examples/dp-secagg-demo/server.py
@@ -61,7 +61,7 @@ def main(driver: Driver, context: Context) -> None:
     # Construct the LegacyContext
     context = LegacyContext(
         state=context.state,
-        config=fl.server.ServerConfig(num_rounds=10),
+        config=fl.server.ServerConfig(num_rounds=3),
         strategy=dp_strategy,
     )
 
diff --git a/examples/dp-secagg-demo/task.py b/examples/dp-secagg-demo/task.py
index 2fe68260c3f5..fa17fe608a9c 100644
--- a/examples/dp-secagg-demo/task.py
+++ b/examples/dp-secagg-demo/task.py
@@ -7,7 +7,6 @@
 import torch.nn.functional as F
 from flwr.common.logger import log
 from torch.utils.data import DataLoader
-from torchvision.datasets import CIFAR10
 from torchvision.transforms import Compose, Normalize, ToTensor
 
 
@@ -15,11 +14,11 @@
 
 
 class Net(nn.Module):
-    """Model (simple CNN adapted from 'PyTorch: A 60 Minute Blitz')"""
+    """Model."""
 
     def __init__(self) -> None:
         super(Net, self).__init__()
-        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.conv1 = nn.Conv2d(1, 6, 3, padding=1)
         self.pool = nn.MaxPool2d(2, 2)
         self.conv2 = nn.Conv2d(6, 16, 5)
         self.fc1 = nn.Linear(16 * 5 * 5, 120)
@@ -27,9 +26,10 @@ def __init__(self) -> None:
         self.fc3 = nn.Linear(84, 10)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
+        batch_size = x.size(0)
         x = self.pool(F.relu(self.conv1(x)))
         x = self.pool(F.relu(self.conv2(x)))
-        x = x.view(-1, 16 * 5 * 5)
+        x = x.view(batch_size, -1)
         x = F.relu(self.fc1(x))
         x = F.relu(self.fc2(x))
         return self.fc3(x)
@@ -37,17 +37,15 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 def load_data(partition_id):
     """Load partition CIFAR10 data."""
-    fds = FederatedDataset(dataset="cifar10", partitioners={"train": 100})
+    fds = FederatedDataset(dataset="mnist", partitioners={"train": 100})
     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))]
-    )
+    pytorch_transforms = Compose([ToTensor(), Normalize((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"]]
+        batch["image"] = [pytorch_transforms(img) for img in batch["image"]]
         return batch
 
     partition_train_test = partition_train_test.with_transform(apply_transforms)
@@ -61,11 +59,11 @@ def train(net, trainloader, valloader, epochs, device):
     log(INFO, "Starting training...")
     net.to(device)  # move model to GPU if available
     criterion = torch.nn.CrossEntropyLoss().to(device)
-    optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
+    optimizer = torch.optim.Adam(net.parameters())
     net.train()
     for _ in range(epochs):
         for batch in trainloader:
-            images = batch["img"].to(device)
+            images = batch["image"].to(device)
             labels = batch["label"].to(device)
             optimizer.zero_grad()
             loss = criterion(net(images), labels)
@@ -91,7 +89,7 @@ def test(net, testloader):
     correct, loss = 0, 0.0
     with torch.no_grad():
         for batch in testloader:
-            images = batch["img"].to(DEVICE)
+            images = batch["image"].to(DEVICE)
             labels = batch["label"].to(DEVICE)
             outputs = net(images.to(DEVICE))
             labels = labels.to(DEVICE)

From abe665b2d348c505e471fa3621322e7237a3c7ba Mon Sep 17 00:00:00 2001
From: Mohammad Naseri <mohammad@flower.ai>
Date: Thu, 14 Mar 2024 16:01:58 +0000
Subject: [PATCH 23/31] Change noise multiplier

---
 examples/dp-secagg-demo/server.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/dp-secagg-demo/server.py b/examples/dp-secagg-demo/server.py
index 47411b7b62fe..c1b9d57d993c 100644
--- a/examples/dp-secagg-demo/server.py
+++ b/examples/dp-secagg-demo/server.py
@@ -48,7 +48,7 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
 )
 
 dp_strategy = DifferentialPrivacyClientSideFixedClipping(
-    strategy, noise_multiplier=0.1, clipping_norm=10, num_sampled_clients=20
+    strategy, noise_multiplier=0.2, clipping_norm=10, num_sampled_clients=20
 )
 
 

From 53f52f39489bf706deac0cd76778619003f7c31a Mon Sep 17 00:00:00 2001
From: "Daniel J. Beutel" <daniel@flower.ai>
Date: Thu, 14 Mar 2024 16:25:53 +0000
Subject: [PATCH 24/31] Organize imports, update comments

---
 examples/dp-secagg-demo/client.py |  7 +++----
 examples/dp-secagg-demo/server.py | 30 +++++++++++++++---------------
 2 files changed, 18 insertions(+), 19 deletions(-)

diff --git a/examples/dp-secagg-demo/client.py b/examples/dp-secagg-demo/client.py
index 6a6114b5e16c..d5213707c920 100644
--- a/examples/dp-secagg-demo/client.py
+++ b/examples/dp-secagg-demo/client.py
@@ -1,9 +1,8 @@
-from task import DEVICE, Net, get_weights, load_data, set_weights, test, train
-
 from flwr.client import ClientApp, NumPyClient
-from flwr.client.mod import secaggplus_mod
+from flwr.client.mod import fixedclipping_mod, secaggplus_mod
+
+from task import DEVICE, Net, get_weights, load_data, set_weights, test, train
 
-from flwr.client.mod.centraldp_mods import fixedclipping_mod
 
 # Load model and data (simple CNN, CIFAR-10)
 net = Net().to(DEVICE)
diff --git a/examples/dp-secagg-demo/server.py b/examples/dp-secagg-demo/server.py
index 47411b7b62fe..51276265a4f8 100644
--- a/examples/dp-secagg-demo/server.py
+++ b/examples/dp-secagg-demo/server.py
@@ -1,14 +1,14 @@
 from typing import List, Tuple
 
-from task import Net, get_weights
-
-import flwr as fl
+from flwr.server import Driver, LegacyContext, ServerApp, ServerConfig
 from flwr.common import Context, Metrics, ndarrays_to_parameters
-from flwr.server import Driver, LegacyContext
-from flwr.server.workflow import SecAggPlusWorkflow
-from flwr.server.strategy.dp_fixed_clipping import (
+from flwr.server.strategy import (
     DifferentialPrivacyClientSideFixedClipping,
+    FedAvg,
 )
+from flwr.server.workflow import DefaultWorkflow, SecAggPlusWorkflow
+
+from task import Net, get_weights
 
 
 # Define metric aggregation function
@@ -37,8 +37,8 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
 parameters = ndarrays_to_parameters(ndarrays)
 
 
-# Define strategy
-strategy = fl.server.strategy.FedAvg(
+# Define core strategy
+strategy = FedAvg(
     fraction_fit=0.2,
     fraction_evaluate=0.0,  # Disable evaluation for demo purpose
     min_fit_clients=20,
@@ -47,13 +47,13 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
     initial_parameters=parameters,
 )
 
-dp_strategy = DifferentialPrivacyClientSideFixedClipping(
+# Wrap the core strategy
+strategy = DifferentialPrivacyClientSideFixedClipping(
     strategy, noise_multiplier=0.1, clipping_norm=10, num_sampled_clients=20
 )
 
 
-# Run via `flower-server-app server_workflow:app`
-app = fl.server.ServerApp()
+app = ServerApp()
 
 
 @app.main()
@@ -61,12 +61,12 @@ def main(driver: Driver, context: Context) -> None:
     # Construct the LegacyContext
     context = LegacyContext(
         state=context.state,
-        config=fl.server.ServerConfig(num_rounds=3),
-        strategy=dp_strategy,
+        config=ServerConfig(num_rounds=3),
+        strategy=strategy,
     )
 
-    # Create the workflow
-    workflow = fl.server.workflow.DefaultWorkflow(
+    # Create the train/evaluate workflow
+    workflow = DefaultWorkflow(
         fit_workflow=SecAggPlusWorkflow(
             num_shares=7,
             reconstruction_threshold=4,

From 23ec20ebaba6dbe3756bcaf441f61fb07625ed3c Mon Sep 17 00:00:00 2001
From: "Daniel J. Beutel" <daniel@flower.ai>
Date: Thu, 14 Mar 2024 17:50:07 +0000
Subject: [PATCH 25/31] Move code into subdir

---
 examples/dp-secagg-demo/fl_dp_sa/__init__.py     | 1 +
 examples/dp-secagg-demo/{ => fl_dp_sa}/client.py | 0
 examples/dp-secagg-demo/{ => fl_dp_sa}/server.py | 0
 examples/dp-secagg-demo/{ => fl_dp_sa}/task.py   | 0
 4 files changed, 1 insertion(+)
 create mode 100644 examples/dp-secagg-demo/fl_dp_sa/__init__.py
 rename examples/dp-secagg-demo/{ => fl_dp_sa}/client.py (100%)
 rename examples/dp-secagg-demo/{ => fl_dp_sa}/server.py (100%)
 rename examples/dp-secagg-demo/{ => fl_dp_sa}/task.py (100%)

diff --git a/examples/dp-secagg-demo/fl_dp_sa/__init__.py b/examples/dp-secagg-demo/fl_dp_sa/__init__.py
new file mode 100644
index 000000000000..dd38275b20df
--- /dev/null
+++ b/examples/dp-secagg-demo/fl_dp_sa/__init__.py
@@ -0,0 +1 @@
+"""fl_dp_secaggplus."""
diff --git a/examples/dp-secagg-demo/client.py b/examples/dp-secagg-demo/fl_dp_sa/client.py
similarity index 100%
rename from examples/dp-secagg-demo/client.py
rename to examples/dp-secagg-demo/fl_dp_sa/client.py
diff --git a/examples/dp-secagg-demo/server.py b/examples/dp-secagg-demo/fl_dp_sa/server.py
similarity index 100%
rename from examples/dp-secagg-demo/server.py
rename to examples/dp-secagg-demo/fl_dp_sa/server.py
diff --git a/examples/dp-secagg-demo/task.py b/examples/dp-secagg-demo/fl_dp_sa/task.py
similarity index 100%
rename from examples/dp-secagg-demo/task.py
rename to examples/dp-secagg-demo/fl_dp_sa/task.py

From 77f21edbf9758d1f2f910b85f637e37fe6741c67 Mon Sep 17 00:00:00 2001
From: "Daniel J. Beutel" <daniel@flower.ai>
Date: Thu, 14 Mar 2024 17:53:08 +0000
Subject: [PATCH 26/31] Update imports

---
 examples/dp-secagg-demo/fl_dp_sa/__init__.py | 2 +-
 examples/dp-secagg-demo/fl_dp_sa/client.py   | 4 +++-
 examples/dp-secagg-demo/fl_dp_sa/server.py   | 4 +++-
 examples/dp-secagg-demo/fl_dp_sa/task.py     | 2 ++
 4 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/examples/dp-secagg-demo/fl_dp_sa/__init__.py b/examples/dp-secagg-demo/fl_dp_sa/__init__.py
index dd38275b20df..741260348ab8 100644
--- a/examples/dp-secagg-demo/fl_dp_sa/__init__.py
+++ b/examples/dp-secagg-demo/fl_dp_sa/__init__.py
@@ -1 +1 @@
-"""fl_dp_secaggplus."""
+"""fl_dp_sa: A Flower / PyTorch app."""
diff --git a/examples/dp-secagg-demo/fl_dp_sa/client.py b/examples/dp-secagg-demo/fl_dp_sa/client.py
index d5213707c920..104264158833 100644
--- a/examples/dp-secagg-demo/fl_dp_sa/client.py
+++ b/examples/dp-secagg-demo/fl_dp_sa/client.py
@@ -1,7 +1,9 @@
+"""fl_dp_sa: A Flower / PyTorch app."""
+
 from flwr.client import ClientApp, NumPyClient
 from flwr.client.mod import fixedclipping_mod, secaggplus_mod
 
-from task import DEVICE, Net, get_weights, load_data, set_weights, test, train
+from fl_dp_sa.task import DEVICE, Net, get_weights, load_data, set_weights, test, train
 
 
 # Load model and data (simple CNN, CIFAR-10)
diff --git a/examples/dp-secagg-demo/fl_dp_sa/server.py b/examples/dp-secagg-demo/fl_dp_sa/server.py
index 602028e76035..58b397f6eb90 100644
--- a/examples/dp-secagg-demo/fl_dp_sa/server.py
+++ b/examples/dp-secagg-demo/fl_dp_sa/server.py
@@ -1,3 +1,5 @@
+"""fl_dp_sa: A Flower / PyTorch app."""
+
 from typing import List, Tuple
 
 from flwr.server import Driver, LegacyContext, ServerApp, ServerConfig
@@ -8,7 +10,7 @@
 )
 from flwr.server.workflow import DefaultWorkflow, SecAggPlusWorkflow
 
-from task import Net, get_weights
+from fl_dp_sa.task import Net, get_weights
 
 
 # Define metric aggregation function
diff --git a/examples/dp-secagg-demo/fl_dp_sa/task.py b/examples/dp-secagg-demo/fl_dp_sa/task.py
index fa17fe608a9c..d091d0b974ea 100644
--- a/examples/dp-secagg-demo/fl_dp_sa/task.py
+++ b/examples/dp-secagg-demo/fl_dp_sa/task.py
@@ -1,3 +1,5 @@
+"""fl_dp_sa: A Flower / PyTorch app."""
+
 from collections import OrderedDict
 from logging import INFO
 from flwr_datasets import FederatedDataset

From 2c52ac7b39c277628dc725293c160c9deef7d3b1 Mon Sep 17 00:00:00 2001
From: "Daniel J. Beutel" <daniel@flower.ai>
Date: Thu, 14 Mar 2024 17:56:29 +0000
Subject: [PATCH 27/31] Add flower.toml/pyproject.toml/requirements.txt

---
 examples/dp-secagg-demo/flower.toml      | 13 +++++++++++++
 examples/dp-secagg-demo/pyproject.toml   | 21 +++++++++++++++++++++
 examples/dp-secagg-demo/requirements.txt |  4 ++++
 3 files changed, 38 insertions(+)
 create mode 100644 examples/dp-secagg-demo/flower.toml
 create mode 100644 examples/dp-secagg-demo/pyproject.toml
 create mode 100644 examples/dp-secagg-demo/requirements.txt

diff --git a/examples/dp-secagg-demo/flower.toml b/examples/dp-secagg-demo/flower.toml
new file mode 100644
index 000000000000..ea2e98206791
--- /dev/null
+++ b/examples/dp-secagg-demo/flower.toml
@@ -0,0 +1,13 @@
+[project]
+name = "fl_dp_sa"
+version = "1.0.0"
+description = ""
+license = "Apache-2.0"
+authors = [
+    "The Flower Authors <hello@flower.ai>",
+]
+readme = "README.md"
+
+[flower.components]
+serverapp = "fl_dp_sa.server:app"
+clientapp = "fl_dp_sa.client:app"
diff --git a/examples/dp-secagg-demo/pyproject.toml b/examples/dp-secagg-demo/pyproject.toml
new file mode 100644
index 000000000000..131e744c5b39
--- /dev/null
+++ b/examples/dp-secagg-demo/pyproject.toml
@@ -0,0 +1,21 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "fl_dp_sa"
+version = "1.0.0"
+description = ""
+license = "Apache-2.0"
+authors = [
+    "The Flower Authors <hello@flower.ai>",
+]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.9"
+# Mandatory dependencies
+flwr-nightly = { version = "1.8.0.dev20240313", extras = ["simulation"] }
+flwr-datasets = { version = "0.0.2", extras = ["vision"] }
+torch = "2.2.1"
+torchvision = "0.17.1"
diff --git a/examples/dp-secagg-demo/requirements.txt b/examples/dp-secagg-demo/requirements.txt
new file mode 100644
index 000000000000..ddb8a814447b
--- /dev/null
+++ b/examples/dp-secagg-demo/requirements.txt
@@ -0,0 +1,4 @@
+flwr-nightly[simulation]==1.8.0.dev20240313
+flwr-datasets[vision]==0.0.2
+torch==2.2.1
+torchvision==0.17.1

From 7c3d1b318bffa3a00a3ff335e181da2727d8c25e Mon Sep 17 00:00:00 2001
From: "Daniel J. Beutel" <daniel@flower.ai>
Date: Thu, 14 Mar 2024 18:03:04 +0000
Subject: [PATCH 28/31] Update README

---
 examples/dp-secagg-demo/README.md | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/examples/dp-secagg-demo/README.md b/examples/dp-secagg-demo/README.md
index d5d5069222a7..99a0a7e50980 100644
--- a/examples/dp-secagg-demo/README.md
+++ b/examples/dp-secagg-demo/README.md
@@ -1,12 +1,22 @@
-# DP + SecAgg Demo Example
+# fl_dp_sa
 
 This is a simple example that utilizes central differential privacy with client-side fixed clipping and secure aggregation.
 Note: This example is designed for a small number of rounds and is intended for demonstration purposes.
 
+## Install dependencies
+
+```bash
+# Using pip
+pip install .
+
+# Or using Poetry
+poetry install
+```
+
 ## Run
 
 The example uses the CIFAR-10 dataset with a total of 100 clients, with 20 clients sampled in each round. The hyperparameters for DP and SecAgg are specified in `server.py`.
 
 ```shell
-flower-simulation --server-app server:app --client-app client:app --num-supernodes 100
+flower-simulation --server-app fl_dp_sa.server:app --client-app fl_dp_sa.client:app --num-supernodes 100
 ```

From ac31041c72b2484a58ae131f4f31fd45647f923d Mon Sep 17 00:00:00 2001
From: "Daniel J. Beutel" <daniel@flower.ai>
Date: Thu, 14 Mar 2024 23:42:58 +0000
Subject: [PATCH 29/31] Rename example

---
 examples/{dp-secagg-demo => fl-dp-sa}/README.md            | 0
 examples/{dp-secagg-demo => fl-dp-sa}/fl_dp_sa/__init__.py | 0
 examples/{dp-secagg-demo => fl-dp-sa}/fl_dp_sa/client.py   | 0
 examples/{dp-secagg-demo => fl-dp-sa}/fl_dp_sa/server.py   | 0
 examples/{dp-secagg-demo => fl-dp-sa}/fl_dp_sa/task.py     | 0
 examples/{dp-secagg-demo => fl-dp-sa}/flower.toml          | 0
 examples/{dp-secagg-demo => fl-dp-sa}/pyproject.toml       | 0
 examples/{dp-secagg-demo => fl-dp-sa}/requirements.txt     | 0
 8 files changed, 0 insertions(+), 0 deletions(-)
 rename examples/{dp-secagg-demo => fl-dp-sa}/README.md (100%)
 rename examples/{dp-secagg-demo => fl-dp-sa}/fl_dp_sa/__init__.py (100%)
 rename examples/{dp-secagg-demo => fl-dp-sa}/fl_dp_sa/client.py (100%)
 rename examples/{dp-secagg-demo => fl-dp-sa}/fl_dp_sa/server.py (100%)
 rename examples/{dp-secagg-demo => fl-dp-sa}/fl_dp_sa/task.py (100%)
 rename examples/{dp-secagg-demo => fl-dp-sa}/flower.toml (100%)
 rename examples/{dp-secagg-demo => fl-dp-sa}/pyproject.toml (100%)
 rename examples/{dp-secagg-demo => fl-dp-sa}/requirements.txt (100%)

diff --git a/examples/dp-secagg-demo/README.md b/examples/fl-dp-sa/README.md
similarity index 100%
rename from examples/dp-secagg-demo/README.md
rename to examples/fl-dp-sa/README.md
diff --git a/examples/dp-secagg-demo/fl_dp_sa/__init__.py b/examples/fl-dp-sa/fl_dp_sa/__init__.py
similarity index 100%
rename from examples/dp-secagg-demo/fl_dp_sa/__init__.py
rename to examples/fl-dp-sa/fl_dp_sa/__init__.py
diff --git a/examples/dp-secagg-demo/fl_dp_sa/client.py b/examples/fl-dp-sa/fl_dp_sa/client.py
similarity index 100%
rename from examples/dp-secagg-demo/fl_dp_sa/client.py
rename to examples/fl-dp-sa/fl_dp_sa/client.py
diff --git a/examples/dp-secagg-demo/fl_dp_sa/server.py b/examples/fl-dp-sa/fl_dp_sa/server.py
similarity index 100%
rename from examples/dp-secagg-demo/fl_dp_sa/server.py
rename to examples/fl-dp-sa/fl_dp_sa/server.py
diff --git a/examples/dp-secagg-demo/fl_dp_sa/task.py b/examples/fl-dp-sa/fl_dp_sa/task.py
similarity index 100%
rename from examples/dp-secagg-demo/fl_dp_sa/task.py
rename to examples/fl-dp-sa/fl_dp_sa/task.py
diff --git a/examples/dp-secagg-demo/flower.toml b/examples/fl-dp-sa/flower.toml
similarity index 100%
rename from examples/dp-secagg-demo/flower.toml
rename to examples/fl-dp-sa/flower.toml
diff --git a/examples/dp-secagg-demo/pyproject.toml b/examples/fl-dp-sa/pyproject.toml
similarity index 100%
rename from examples/dp-secagg-demo/pyproject.toml
rename to examples/fl-dp-sa/pyproject.toml
diff --git a/examples/dp-secagg-demo/requirements.txt b/examples/fl-dp-sa/requirements.txt
similarity index 100%
rename from examples/dp-secagg-demo/requirements.txt
rename to examples/fl-dp-sa/requirements.txt

From 3e1a121bc04ea6ae78cbeebcadd54e6b5764723d Mon Sep 17 00:00:00 2001
From: "Daniel J. Beutel" <daniel@flower.ai>
Date: Thu, 14 Mar 2024 23:45:34 +0000
Subject: [PATCH 30/31] Update pyproject.toml

---
 examples/fl-dp-sa/pyproject.toml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/fl-dp-sa/pyproject.toml b/examples/fl-dp-sa/pyproject.toml
index 131e744c5b39..d30fa4675e34 100644
--- a/examples/fl-dp-sa/pyproject.toml
+++ b/examples/fl-dp-sa/pyproject.toml
@@ -3,8 +3,8 @@ requires = ["poetry-core>=1.4.0"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
-name = "fl_dp_sa"
-version = "1.0.0"
+name = "fl-dp-sa"
+version = "0.1.0"
 description = ""
 license = "Apache-2.0"
 authors = [

From 3ffe3f5dd1dec68f8839c1fd2ee63e863ee7eb9a Mon Sep 17 00:00:00 2001
From: "Daniel J. Beutel" <daniel@flower.ai>
Date: Fri, 15 Mar 2024 00:20:37 +0000
Subject: [PATCH 31/31] Remove log

---
 examples/fl-dp-sa/fl_dp_sa/server.py | 5 +----
 examples/fl-dp-sa/fl_dp_sa/task.py   | 1 -
 2 files changed, 1 insertion(+), 5 deletions(-)

diff --git a/examples/fl-dp-sa/fl_dp_sa/server.py b/examples/fl-dp-sa/fl_dp_sa/server.py
index 58b397f6eb90..f7da75997e98 100644
--- a/examples/fl-dp-sa/fl_dp_sa/server.py
+++ b/examples/fl-dp-sa/fl_dp_sa/server.py
@@ -39,7 +39,7 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
 parameters = ndarrays_to_parameters(ndarrays)
 
 
-# Define core strategy
+# Define strategy
 strategy = FedAvg(
     fraction_fit=0.2,
     fraction_evaluate=0.0,  # Disable evaluation for demo purpose
@@ -48,9 +48,6 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
     fit_metrics_aggregation_fn=weighted_average,
     initial_parameters=parameters,
 )
-
-
-# Wrap the core strategy
 strategy = DifferentialPrivacyClientSideFixedClipping(
     strategy, noise_multiplier=0.2, clipping_norm=10, num_sampled_clients=20
 )
diff --git a/examples/fl-dp-sa/fl_dp_sa/task.py b/examples/fl-dp-sa/fl_dp_sa/task.py
index d091d0b974ea..3d506263d5a3 100644
--- a/examples/fl-dp-sa/fl_dp_sa/task.py
+++ b/examples/fl-dp-sa/fl_dp_sa/task.py
@@ -58,7 +58,6 @@ def apply_transforms(batch):
 
 def train(net, trainloader, valloader, epochs, device):
     """Train the model on the training set."""
-    log(INFO, "Starting training...")
     net.to(device)  # move model to GPU if available
     criterion = torch.nn.CrossEntropyLoss().to(device)
     optimizer = torch.optim.Adam(net.parameters())