Merge branch 'main' into fds-rename-size-partitioner

dev/changelog_config.toml

@@ -3,7 +3,7 @@
 
 type = ["ci", "docs", "feat", "fix", "refactor", "break"]
 
-project = ["framework", "baselines", "datasets", "examples", "benchmarks"]
+project = ["framework", "baselines", "datasets", "examples", "benchmarks", "glossary"]
 
 scope = "skip"
 

doc/source/how-to-authenticate-supernodes.rst

@@ -28,13 +28,13 @@ Use the following terminal command to start a Flower :code:`SuperNode` that has
 .. code-block:: bash
 
     flower-superlink
-        --ssl-ca-certfile certificates/ca.crt 
-        --ssl-certfile certificates/server.pem 
+        --ssl-ca-certfile certificates/ca.crt
+        --ssl-certfile certificates/server.pem
         --ssl-keyfile certificates/server.key
         --auth-list-public-keys keys/client_public_keys.csv
         --auth-superlink-private-key keys/server_credentials
         --auth-superlink-public-key keys/server_credentials.pub
-    
+
 Let's break down the authentication flags:
 
 1. The first flag :code:`--auth-list-public-keys` expects a path to a CSV file storing all known node public keys. You need to store all known node public keys that are allowed to participate in a federation in one CSV file (:code:`.csv`).
@@ -56,8 +56,8 @@ Similar to the long-running Flower server (:code:`SuperLink`), you can easily en
 Use the following terminal command to start an authenticated :code:`SuperNode`:
 
 .. code-block:: bash
-    
-    flower-client-app client:app
+
+   flower-supernode
         --root-certificates certificates/ca.crt
         --superlink 127.0.0.1:9092
         --auth-supernode-private-key keys/client_credentials

doc/source/how-to-enable-ssl-connections.rst

@@ -4,7 +4,7 @@ Enable SSL connections
 This guide describes how to a SSL-enabled secure Flower server (:code:`SuperLink`) can be started and
 how a Flower client (:code:`SuperNode`) can establish a secure connections to it.
 
-A complete code example demonstrating a secure connection can be found 
+A complete code example demonstrating a secure connection can be found
 `here <https://github.com/adap/flower/tree/main/examples/advanced-tensorflow>`_.
 
 The code example comes with a :code:`README.md` file which explains how to start it. Although it is
@@ -42,9 +42,9 @@ Use the following terminal command to start a sever (SuperLink) that uses the pr
 
 .. code-block:: bash
 
-    flower-superlink 
-      --ssl-ca-certfile certificates/ca.crt 
-      --ssl-certfile certificates/server.pem 
+    flower-superlink
+      --ssl-ca-certfile certificates/ca.crt
+      --ssl-certfile certificates/server.pem
       --ssl-keyfile certificates/server.key
 
 When providing certificates, the server expects a tuple of three certificates paths: CA certificate, server certificate and server private key.
@@ -57,7 +57,7 @@ Use the following terminal command to start a client (SuperNode) that uses the p
 
 .. code-block:: bash
 
-    flower-client-app client:app
+   flower-supernode
         --root-certificates certificates/ca.crt
         --superlink 127.0.0.1:9092
 

src/proto/flwr/proto/clientappio.proto

@@ -1,3 +1,18 @@
+// Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// ==============================================================================
+
 syntax = "proto3";
 
 package flwr.proto;

src/proto/flwr/proto/fab.proto

@@ -1,3 +1,18 @@
+// Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// ==============================================================================
+
 syntax = "proto3";
 
 package flwr.proto;

src/py/flwr/cli/new/new.py

@@ -136,36 +136,23 @@ def new(
         username = prompt_text("Please provide your Flower username")
 
     if framework is not None:
-        framework_str_upper = str(framework.value)
+        framework_str = str(framework.value)
     else:
-        framework_value = prompt_options(
+        framework_str = prompt_options(
             "Please select ML framework by typing in the number",
             [mlf.value for mlf in MlFramework],
         )
-        selected_value = [
-            name
-            for name, value in vars(MlFramework).items()
-            if value == framework_value
-        ]
-        framework_str_upper = selected_value[0]
-
-    framework_str = framework_str_upper.lower()
 
     llm_challenge_str = None
-    if framework_str == "flowertune":
+    if framework_str == MlFramework.FLOWERTUNE:
         llm_challenge_value = prompt_options(
             "Please select LLM challenge by typing in the number",
             sorted([challenge.value for challenge in LlmChallengeName]),
         )
-        selected_value = [
-            name
-            for name, value in vars(LlmChallengeName).items()
-            if value == llm_challenge_value
-        ]
-        llm_challenge_str = selected_value[0]
-        llm_challenge_str = llm_challenge_str.lower()
+        llm_challenge_str = llm_challenge_value.lower()
 
-    is_baseline_project = framework_str == "baseline"
+    if framework_str == MlFramework.BASELINE:
+        framework_str = "baseline"
 
     print(
         typer.style(
@@ -176,19 +163,21 @@ def new(
     )
 
     context = {
-        "framework_str": framework_str_upper,
+        "framework_str": framework_str,
         "import_name": import_name.replace("-", "_"),
         "package_name": package_name,
         "project_name": app_name,
         "username": username,
     }
 
+    template_name = framework_str.lower()
+
     # List of files to render
     if llm_challenge_str:
         files = {
             ".gitignore": {"template": "app/.gitignore.tpl"},
-            "pyproject.toml": {"template": f"app/pyproject.{framework_str}.toml.tpl"},
-            "README.md": {"template": f"app/README.{framework_str}.md.tpl"},
+            "pyproject.toml": {"template": f"app/pyproject.{template_name}.toml.tpl"},
+            "README.md": {"template": f"app/README.{template_name}.md.tpl"},
             f"{import_name}/__init__.py": {"template": "app/code/__init__.py.tpl"},
             f"{import_name}/server_app.py": {
                 "template": "app/code/flwr_tune/server_app.py.tpl"
@@ -235,44 +224,42 @@ def new(
         files = {
             ".gitignore": {"template": "app/.gitignore.tpl"},
             "README.md": {"template": "app/README.md.tpl"},
-            "pyproject.toml": {"template": f"app/pyproject.{framework_str}.toml.tpl"},
+            "pyproject.toml": {"template": f"app/pyproject.{template_name}.toml.tpl"},
             f"{import_name}/__init__.py": {"template": "app/code/__init__.py.tpl"},
             f"{import_name}/server_app.py": {
-                "template": f"app/code/server.{framework_str}.py.tpl"
+                "template": f"app/code/server.{template_name}.py.tpl"
             },
             f"{import_name}/client_app.py": {
-                "template": f"app/code/client.{framework_str}.py.tpl"
+                "template": f"app/code/client.{template_name}.py.tpl"
             },
         }
 
         # Depending on the framework, generate task.py file
         frameworks_with_tasks = [
-            MlFramework.PYTORCH.value.lower(),
-            MlFramework.JAX.value.lower(),
-            MlFramework.HUGGINGFACE.value.lower(),
-            MlFramework.MLX.value.lower(),
-            MlFramework.TENSORFLOW.value.lower(),
+            MlFramework.PYTORCH.value,
+            MlFramework.JAX.value,
+            MlFramework.HUGGINGFACE.value,
+            MlFramework.MLX.value,
+            MlFramework.TENSORFLOW.value,
         ]
         if framework_str in frameworks_with_tasks:
             files[f"{import_name}/task.py"] = {
-                "template": f"app/code/task.{framework_str}.py.tpl"
+                "template": f"app/code/task.{template_name}.py.tpl"
             }
 
-        if is_baseline_project:
+        if framework_str == "baseline":
             # Include additional files for baseline template
             for file_name in ["model", "dataset", "strategy", "utils", "__init__"]:
                 files[f"{import_name}/{file_name}.py"] = {
-                    "template": f"app/code/{file_name}.{framework_str}.py.tpl"
+                    "template": f"app/code/{file_name}.{template_name}.py.tpl"
                 }
 
             # Replace README.md
-            files["README.md"]["template"] = f"app/README.{framework_str}.md.tpl"
+            files["README.md"]["template"] = f"app/README.{template_name}.md.tpl"
 
             # Add LICENSE
             files["LICENSE"] = {"template": "app/LICENSE.tpl"}
 
-            context["framework_str"] = "baseline"
-
     for file_path, value in files.items():
         render_and_create(
             file_path=project_dir / file_path,

src/py/flwr/cli/new/templates/app/code/client.huggingface.py.tpl

@@ -1,18 +1,11 @@
 """$project_name: A Flower / $framework_str app."""
 
+import torch
 from flwr.client import ClientApp, NumPyClient
 from flwr.common import Context
 from transformers import AutoModelForSequenceClassification
 
-from $import_name.task import (
-    get_weights,
-    load_data,
-    set_weights,
-    train,
-    test,
-    CHECKPOINT,
-    DEVICE,
-)
+from $import_name.task import get_weights, load_data, set_weights, test, train
 
 
 # Flower client
@@ -22,37 +15,34 @@ class FlowerClient(NumPyClient):
         self.trainloader = trainloader
         self.testloader = testloader
         self.local_epochs = local_epochs
-
-    def get_parameters(self, config):
-        return get_weights(self.net)
-
-    def set_parameters(self, parameters):
-        set_weights(self.net, parameters)
+        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        self.net.to(self.device)
 
     def fit(self, parameters, config):
-        self.set_parameters(parameters)
-        train(
-            self.net,
-            self.trainloader,
-            epochs=self.local_epochs,
-        )
-        return self.get_parameters(config={}), len(self.trainloader), {}
+        set_weights(self.net, parameters)
+        train(self.net, self.trainloader, epochs=self.local_epochs, device=self.device)
+        return get_weights(self.net), len(self.trainloader), {}
 
     def evaluate(self, parameters, config):
-        self.set_parameters(parameters)
-        loss, accuracy = test(self.net, self.testloader)
+        set_weights(self.net, parameters)
+        loss, accuracy = test(self.net, self.testloader, self.device)
         return float(loss), len(self.testloader), {"accuracy": accuracy}
 
 
 def client_fn(context: Context):
-    # Load model and data
-    net = AutoModelForSequenceClassification.from_pretrained(
-        CHECKPOINT, num_labels=2
-    ).to(DEVICE)
 
+    # Get this client's dataset partition
     partition_id = context.node_config["partition-id"]
     num_partitions = context.node_config["num-partitions"]
-    trainloader, valloader = load_data(partition_id, num_partitions)
+    model_name = context.run_config["model-name"]
+    trainloader, valloader = load_data(partition_id, num_partitions, model_name)
+
+    # Load model
+    num_labels = context.run_config["num-labels"]
+    net = AutoModelForSequenceClassification.from_pretrained(
+        model_name, num_labels=num_labels
+    )
+
     local_epochs = context.run_config["local-epochs"]
 
     # Return Client instance

src/py/flwr/cli/new/templates/app/code/server.huggingface.py.tpl

@@ -1,18 +1,33 @@
 """$project_name: A Flower / $framework_str app."""
 
-from flwr.common import Context
-from flwr.server.strategy import FedAvg
+from flwr.common import Context, ndarrays_to_parameters
 from flwr.server import ServerApp, ServerAppComponents, ServerConfig
+from flwr.server.strategy import FedAvg
+from transformers import AutoModelForSequenceClassification
+
+from $import_name.task import get_weights
 
 
 def server_fn(context: Context):
     # Read from config
     num_rounds = context.run_config["num-server-rounds"]
+    fraction_fit = context.run_config["fraction-fit"]
+
+    # Initialize global model
+    model_name = context.run_config["model-name"]
+    num_labels = context.run_config["num-labels"]
+    net = AutoModelForSequenceClassification.from_pretrained(
+        model_name, num_labels=num_labels
+    )
+
+    weights = get_weights(net)
+    initial_parameters = ndarrays_to_parameters(weights)
 
     # Define strategy
     strategy = FedAvg(
-        fraction_fit=1.0,
+        fraction_fit=fraction_fit,
         fraction_evaluate=1.0,
+        initial_parameters=initial_parameters,
     )
     config = ServerConfig(num_rounds=num_rounds)