Example for Custom Metrics calculation during Federated Learning

examples/custom-metrics/README.md

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+# Flower Example using Custom Metrics
+
+This simple example demonstrates how to calculate custom metrics over multiple clients beyond the traditional ones available in the ML frameworks. In this case, it demonstrates the use of ready-available `scikit-learn` metrics: accuracy, recall, precision, and f1-score.
+
+Once both the test values (`y_test`) and the predictions (`y_pred`) are available on the client side (`client.py`), other metrics or custom ones are possible to be calculated.
+
+The main takeaways of this implementation are:
+
+- the use of the `output_dict` on the client side - inside `evaluate` method on `client.py`
+- the use of the `evaluate_metrics_aggregation_fn` - to aggregate the metrics on the server side, part of the `strategy` on `server.py`
+
+This example is based on the `quickstart-tensorflow` with CIFAR-10, source [here](https://flower.dev/docs/quickstart-tensorflow.html), with the addition of [Flower Datasets](https://flower.dev/docs/datasets/index.html) to retrieve the CIFAR-10.
+
+Using the CIFAR-10 dataset for classification, this is a multi-class classification problem, thus some changes on how to calculate the metrics using `average='micro'` and `np.argmax` is required. For binary classification, this is not required. Also, for unsupervised learning tasks, such as using a deep autoencoder, a custom metric based on reconstruction error could be implemented on client side.
+
+## Project Setup
+
+Start by cloning the example project. We prepared a single-line command that you can copy into your shell which will checkout the example for you:
+
+```shell
+git clone --depth=1 https://github.com/adap/flower.git && mv flower/examples/custom-metrics . && rm -rf flower && cd custom-metrics
+```
+
+This will create a new directory called `custom-metrics` containing the following files:
+
+```shell
+-- pyproject.toml
+-- requirements.txt
+-- client.py
+-- server.py
+-- run.sh
+-- README.md
+```
+
+### Installing Dependencies
+
+Project dependencies (such as `scikit-learn`, `tensorflow` and `flwr`) are defined in `pyproject.toml` and `requirements.txt`. We recommend [Poetry](https://python-poetry.org/docs/) to install those dependencies and manage your virtual environment ([Poetry installation](https://python-poetry.org/docs/#installation)) or [pip](https://pip.pypa.io/en/latest/development/), but feel free to use a different way of installing dependencies and managing virtual environments if you have other preferences.
+
+#### Poetry
+
+```shell
+poetry install
+poetry shell
+```
+
+Poetry will install all your dependencies in a newly created virtual environment. To verify that everything works correctly you can run the following command:
+
+```shell
+poetry run python3 -c "import flwr"
+```
+
+If you don't see any errors you're good to go!
+
+#### pip
+
+Write the command below in your terminal to install the dependencies according to the configuration file requirements.txt.
+
+```shell
+python -m venv venv
+source venv/bin/activate
+pip install -r requirements.txt
+```
+
+## Run Federated Learning with Custom Metrics
+
+Afterwards you are ready to start the Flower server as well as the clients. You can simply start the server in a terminal as follows:
+
+```shell
+python server.py
+```
+
+Now you are ready to start the Flower clients which will participate in the learning. To do so simply open two more terminals and run the following command in each:
+
+```shell
+python client.py
+```
+
+Alternatively you can run all of it in one shell as follows:
+
+```shell
+python server.py &
+# Wait for a few seconds to give the server enough time to start, then:
+python client.py &
+python client.py
+```
+
+or
+
+```shell
+chmod +x run.sh
+./run.sh
+```
+
+You will see that Keras is starting a federated training. Have a look to the [Flower Quickstarter documentation](https://flower.dev/docs/quickstart-tensorflow.html) for a detailed explanation. You can add `steps_per_epoch=3` to `model.fit()` if you just want to evaluate that everything works without having to wait for the client-side training to finish (this will save you a lot of time during development).
+
+Running `run.sh` will result in the following output (after 3 rounds):
+
+```shell
+INFO flwr 2024-01-17 17:45:23,794 | app.py:228 | app_fit: metrics_distributed {
+    'accuracy': [(1, 0.10000000149011612), (2, 0.10000000149011612), (3, 0.3393000066280365)], 
+    'acc': [(1, 0.1), (2, 0.1), (3, 0.3393)], 
+    'rec': [(1, 0.1), (2, 0.1), (3, 0.3393)], 
+    'prec': [(1, 0.1), (2, 0.1), (3, 0.3393)], 
+    'f1': [(1, 0.10000000000000002), (2, 0.10000000000000002), (3, 0.3393)]
+}
+```

examples/custom-metrics/client.py

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+import os
+
+import flwr as fl
+import numpy as np
+import tensorflow as tf
+from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score
+from flwr_datasets import FederatedDataset
+
+
+# Make TensorFlow log less verbose
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+
+# Load model (MobileNetV2)
+model = tf.keras.applications.MobileNetV2((32, 32, 3), classes=10, weights=None)
+model.compile("adam", "sparse_categorical_crossentropy", metrics=["accuracy"])
+
+# Load data with Flower Datasets (CIFAR-10)
+fds = FederatedDataset(dataset="cifar10", partitioners={"train": 10})
+train = fds.load_full("train")
+test = fds.load_full("test")
+
+# Using Numpy format
+train_np = train.with_format("numpy")
+test_np = test.with_format("numpy")
+x_train, y_train = train_np["img"], train_np["label"]
+x_test, y_test = test_np["img"], test_np["label"]
+
+
+# Method for extra learning metrics calculation
+def eval_learning(y_test, y_pred):
+    acc = accuracy_score(y_test, y_pred)
+    rec = recall_score(
+        y_test, y_pred, average="micro"
+    )  # average argument required for multi-class
+    prec = precision_score(y_test, y_pred, average="micro")
+    f1 = f1_score(y_test, y_pred, average="micro")
+    return acc, rec, prec, f1
+
+
+# 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)
+        return model.get_weights(), len(x_train), {}
+
+    def evaluate(self, parameters, config):
+        model.set_weights(parameters)
+        loss, accuracy = model.evaluate(x_test, y_test)
+        y_pred = model.predict(x_test)
+        y_pred = np.argmax(y_pred, axis=1).reshape(
+            -1, 1
+        )  # MobileNetV2 outputs 10 possible classes, argmax returns just the most probable
+
+        acc, rec, prec, f1 = eval_learning(y_test, y_pred)
+        output_dict = {
+            "accuracy": accuracy,  # accuracy from tensorflow model.evaluate
+            "acc": acc,
+            "rec": rec,
+            "prec": prec,
+            "f1": f1,
+        }
+        return loss, len(x_test), output_dict
+
+
+# Start Flower client
+fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=FlowerClient())

examples/custom-metrics/pyproject.toml

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+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "custom-metrics"
+version = "0.1.0"
+description = "Federated Learning with Flower and Custom Metrics"
+authors = [
+	"The Flower Authors <hello@flower.dev>",
+	"Gustavo Bertoli <gubertoli -at- gmail.com>"
+]
+
+[tool.poetry.dependencies]
+python = ">=3.8,<3.11"
+flwr = ">=1.0,<2.0"
+flwr-datasets = { version = "*", extras = ["vision"] }
+scikit-learn = "^1.2.2"
+tensorflow = "==2.12.0"

examples/custom-metrics/requirements.txt

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+flwr>=1.0,<2.0
+flwr-datasets[vision]
+scikit-learn>=1.2.2
+tensorflow==2.12.0

examples/custom-metrics/run.sh

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+#!/bin/bash
+
+echo "Starting server"
+python server.py &
+sleep 3  # Sleep for 3s to give the server enough time to start
+
+for i in `seq 0 1`; do
+    echo "Starting client $i"
+    python client.py &
+done
+
+# This will allow you to use CTRL+C to stop all background processes
+trap "trap - SIGTERM && kill -- -$$" SIGINT SIGTERM
+# Wait for all background processes to complete
+wait

examples/custom-metrics/server.py

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+import flwr as fl
+import numpy as np
+
+
+# Define metrics aggregation function
+def average_metrics(metrics):
+    """Aggregate metrics from multiple clients by calculating mean averages.
+
+    Parameters:
+    - metrics (list): A list containing tuples, where each tuple represents metrics for a client.
+                    Each tuple is structured as (num_examples, metric), where:
+                    - num_examples (int): The number of examples used to compute the metrics.
+                    - metric (dict): A dictionary containing custom metrics provided as `output_dict`
+                                    in the `evaluate` method from `client.py`.
+
+    Returns:
+    A dictionary with the aggregated metrics, calculating mean averages. The keys of the
+    dictionary represent different metrics, including:
+    - 'accuracy': Mean accuracy calculated by TensorFlow.
+    - 'acc': Mean accuracy from scikit-learn.
+    - 'rec': Mean recall from scikit-learn.
+    - 'prec': Mean precision from scikit-learn.
+    - 'f1': Mean F1 score from scikit-learn.
+
+    Note: If a weighted average is required, the `num_examples` parameter can be leveraged.
+
+    Example:
+        Example `metrics` list for two clients after the last round:
+        [(10000, {'prec': 0.108, 'acc': 0.108, 'f1': 0.108, 'accuracy': 0.1080000028014183, 'rec': 0.108}),
+        (10000, {'f1': 0.108, 'rec': 0.108, 'accuracy': 0.1080000028014183, 'prec': 0.108, 'acc': 0.108})]
+    """
+
+    # Here num_examples are not taken into account by using _
+    accuracies_tf = np.mean([metric["accuracy"] for _, metric in metrics])
+    accuracies = np.mean([metric["acc"] for _, metric in metrics])
+    recalls = np.mean([metric["rec"] for _, metric in metrics])
+    precisions = np.mean([metric["prec"] for _, metric in metrics])
+    f1s = np.mean([metric["f1"] for _, metric in metrics])
+
+    return {
+        "accuracy": accuracies_tf,
+        "acc": accuracies,
+        "rec": recalls,
+        "prec": precisions,
+        "f1": f1s,
+    }
+
+
+# Define strategy and the custom aggregation function for the evaluation metrics
+strategy = fl.server.strategy.FedAvg(evaluate_metrics_aggregation_fn=average_metrics)
+
+
+# Start Flower server
+fl.server.start_server(
+    server_address="0.0.0.0:8080",
+    config=fl.server.ServerConfig(num_rounds=3),
+    strategy=strategy,
+)