Add E2E tests for different strategies

.github/workflows/e2e.yml

@@ -12,7 +12,7 @@ env:
   FLWR_TELEMETRY_ENABLED: 0
 
 jobs:
-  e2e:
+  frameworks:
     runs-on: ubuntu-22.04
     timeout-minutes: 10
     # Using approach described here:
@@ -71,7 +71,7 @@ jobs:
               Path('data').mkdir(exist_ok=True)
               load_iris(as_frame=True)['data'].to_csv('./data/client.csv')
 
-    name: ${{matrix.directory}}
+    name: Framework / ${{matrix.directory}}
 
     defaults:
       run:
@@ -92,3 +92,35 @@ jobs:
         run: ./test.sh
       - name: Run virtual client test 
         run: python simulation.py
+
+  strategies:
+    runs-on: ubuntu-22.04
+    timeout-minutes: 10
+    strategy:
+      matrix:
+        strat: ["FedMedian", "FedTrimmedAvg", "QFedAvg", "FaultTolerantFedAvg", "FedAvgM", "FedAdam", "FedAdagrad", "FedYogi"]
+
+    name: Strategy / ${{ matrix.strat }}
+
+    defaults:
+      run:
+        working-directory: e2e/strategies
+
+    steps:
+      - uses: actions/checkout@v3
+      - name: Bootstrap
+        uses: ./.github/actions/bootstrap
+      - name: Install dependencies
+        run: |
+          python -m poetry install
+      - name: Cache Datasets
+        uses: actions/cache@v2
+        with:
+          path: "~/.keras"
+          key: keras-datasets
+      - name: Download Datasets
+        run: |
+          python -c "import tensorflow as tf; tf.keras.datasets.mnist.load_data()"
+      - name: Test strategies
+        run: |
+          python test.py "${{ matrix.strat }}"

e2e/strategies/README.md

@@ -0,0 +1,16 @@
+# Flower with Tensorflow and multiple strategies testing
+
+This directory is used for testing Flower with Tensorflow by using the CIFAR10 dataset and a CNN.
+
+It uses a subset of size 1000 for the training data and 10 data points for the testing.
+
+It tests the following strategies:
+
+- FedMedian
+- FedTrimmedAvg
+- QFedAvg
+- FaultTolerantFedAvg
+- FedAvgM
+- FedAdam
+- FedAdagrad
+- FedYogi

e2e/strategies/client.py

@@ -0,0 +1,48 @@
+import os
+
+import flwr as fl
+import tensorflow as tf
+
+SUBSET_SIZE = 1000
+
+# Make TensorFlow log less verbose
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+def get_model():
+    model = tf.keras.models.Sequential([
+      tf.keras.layers.Flatten(input_shape=(28, 28)),
+      tf.keras.layers.Dense(128, activation='relu'),
+      tf.keras.layers.Dense(10)
+    ])
+    model.compile(
+        optimizer=tf.keras.optimizers.Adam(0.001),
+        loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+        metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],
+    )
+    return model
+
+model = get_model()
+(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
+x_train, y_train = x_train[:SUBSET_SIZE], y_train[:SUBSET_SIZE]
+x_test, y_test = x_test[:SUBSET_SIZE], y_test[:SUBSET_SIZE]
+
+
+# Define Flower client
+class FlowerClient(fl.client.NumPyClient):
+    def get_parameters(self, config):
+        return model.get_weights()
+
+    def fit(self, parameters, config):
+        model.set_weights(parameters)
+        model.fit(x_train, y_train, epochs=1, batch_size=32, verbose=0)
+        return model.get_weights(), len(x_train), {}
+
+    def evaluate(self, parameters, config):
+        model.set_weights(parameters)
+        loss, accuracy = model.evaluate(x_test, y_test, verbose=0)
+        return loss, len(x_test), {"accuracy": accuracy}
+
+
+if __name__ == "__main__":
+    # Start Flower client
+    fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=FlowerClient())

e2e/strategies/pyproject.toml

@@ -0,0 +1,14 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "quickstart_tensorflow"
+version = "0.1.0"
+description = "Keras Federated Learning Quickstart with Flower"
+authors = ["The Flower Authors <hello@flower.dev>"]
+
+[tool.poetry.dependencies]
+python = ">=3.7,<3.11"
+flwr = { path = "../../", develop = true, extras = ["simulation"] }
+tensorflow-cpu = "^2.9.1, !=2.11.1"

e2e/strategies/test.py

@@ -0,0 +1,84 @@
+from sys import argv
+
+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.server.strategy import (
+    FaultTolerantFedAvg,
+    FedAdagrad,
+    FedAdam,
+    FedAvgM,
+    FedMedian,
+    FedTrimmedAvg,
+    FedYogi,
+    QFedAvg,
+)
+
+STRATEGY_LIST = [
+    FedMedian,
+    FedTrimmedAvg,
+    QFedAvg,
+    FaultTolerantFedAvg,
+    FedAvgM,
+    FedAdam,
+    FedAdagrad,
+    FedYogi,
+]
+OPT_IDX = 5
+
+strat = argv[1]
+
+
+def get_strat(name):
+    return [
+        (idx, strat)
+        for idx, strat in enumerate(STRATEGY_LIST)
+        if strat.__name__ == name
+    ][0]
+
+
+init_model = get_model()
+
+
+def client_fn(cid):
+    _ = cid
+    return FlowerClient()
+
+
+def evaluate(server_round, parameters, config):
+    model = get_model()
+
+    _, (x_test, y_test) = tf.keras.datasets.mnist.load_data()
+    x_test, y_test = x_test[:SUBSET_SIZE], y_test[:SUBSET_SIZE]
+
+    model.set_weights(parameters)
+
+    loss, accuracy = model.evaluate(x_test, y_test)
+
+    # return statistics
+    return loss, {"accuracy": accuracy}
+
+
+strat_args = {
+    "evaluate_fn": evaluate,
+    "initial_parameters": ndarrays_to_parameters(init_model.get_weights()),
+}
+
+start_idx, strategy = get_strat(strat)
+
+if start_idx >= OPT_IDX:
+    strat_args["tau"] = 0.01
+
+hist = fl.simulation.start_simulation(
+    client_fn=client_fn,
+    num_clients=2,
+    config=fl.server.ServerConfig(num_rounds=3),
+    strategy=strategy(**strat_args),
+)
+
+assert (
+    hist.metrics_centralized["accuracy"][0][1]
+    / hist.metrics_centralized["accuracy"][-1][1]
+) <= 1.04 or (hist.losses_centralized[0][1] / hist.losses_centralized[-1][1]) >= 0.96