Communicate custom Messages

Welcome to the fourth part of the Flower federated learning tutorial. In the previous parts of this tutorial, we introduced federated learning with PyTorch and Flower (part 1), we learned how strategies can be used to customize the execution on both the server and the clients (part 2) and we built our own custom strategy from scratch (part 3).

In this final tutorial, we turn our attention again to the ClientApp and show how to communicate arbitrary Python objects via a Message and how to use it on the ServerApp. This can be useful if you want to send additional information between ClientApp <--> ServerApp without the need for custom protocols.

팁

Star Flower on GitHub ⭐️ and join the Flower community on Flower Discuss and the Flower Slack to connect, ask questions, and get help:

• Join Flower Discuss We’d love to hear from you in the Introduction topic! If anything is unclear, post in Flower Help - Beginners.

• Join Flower Slack We’d love to hear from you in the #introductions channel! If anything is unclear, head over to the #questions channel.

Let’s go deeper and see how to serialize arbitrary Python objects and communicate them! 🌼

Preparation

Before we begin with the actual code, let’s make sure that we have everything we need.

Installing dependencies

참고

If you’ve completed part 1 of the tutorial, you can skip this step.

First, we install the Flower package flwr:

# In a new Python environment
$ pip install -U "flwr[simulation]"

Then, we create a new Flower app called flower-tutorial using the PyTorch template. We also specify a username (flwrlabs) for the project:

$ flwr new flower-tutorial --framework pytorch --username flwrlabs

After running the command, a new directory called flower-tutorial will be created. It should have the following structure:

flower-tutorial
├── README.md
├── flower_tutorial
│   ├── __init__.py
│   ├── client_app.py   # Defines your ClientApp
│   ├── server_app.py   # Defines your ServerApp
│   └── task.py         # Defines your model, training and data loading
└── pyproject.toml      # Project metadata like dependencies and configs

Next, we install the project and its dependencies, which are specified in the pyproject.toml file:

$ cd flower-tutorial
$ pip install -e .

Revisiting replying from ClientApps

Let’s remind ourselves how the communication between ClientApp and ServerApp works. A ClientApp function wrapped with @app.train() would typically return the locally updated model parameters in addition to some metrics relevant to the training process, such as the training loss and accuracy. In code, this would look like:

@app.train()
def train(msg: Message, context: Context):
    """Train the model on local data."""

    # ... prepare model, load data, train locally

    # Construct and return reply Message
    model_record = ArrayRecord(model.state_dict())
    metrics = {
        "train_loss": train_loss,
        "num-examples": len(trainloader.dataset),
    }
    metric_record = MetricRecord(metrics)
    content = RecordDict({"arrays": model_record, "metrics": metric_record})
    return Message(content=content, reply_to=msg)

Then, on the ServerApp, the Flower strategy will automatically aggregate the ArrayRecord and MetricRecord from each client into a single ArrayRecord and MetricRecord that can be used to update the global model and log the aggregated metrics. Now, what if we wanted to send additional information from the ClientApp to the ServerApp? For example, let’s say we want to send how long the execution of the ClientApp took. We can do this by adding a new metric to the MetricRecord. It will also be aggregated automatically by the strategy. If you do for example:

import time


@app.train()
def train(msg: Message, context: Context):
    """Train the model on local data."""

    start_time = time.time()

    # ... prepare model, load data, train locally

    end_time = time.time()
    training_time = end_time - start_time

    # Construct and return reply Message
    model_record = ArrayRecord(model.state_dict())
    metrics = {
        "train_loss": train_loss,
        "num-examples": len(trainloader.dataset),
        "training_time": training_time,  # New metric
    }
    metric_record = MetricRecord(metrics)
    content = RecordDict({"arrays": model_record, "metrics": metric_record})
    return Message(content=content, reply_to=msg)

If you’d like to communicate other types of objects and leave them out of the aggregation process, you can use a ConfigRecord. In addition to integers and floats, you can use a ConfigRecord to send strings, booleans and even bytes. In the next section we’ll learn to communicate arbitrary Python objects by first serializing them to bytes.

Communicating arbitrary objects

Let’s assume the training stage of our ClientApp produces a dataclass like the one below and we would like to communicate it to the ServerApp via the Message. Let’s go ahead and define this in task.py:

from dataclasses import dataclass


@dataclass
class TrainProcessMetadata:
    """Metadata about the training process."""

    training_time: float
    converged: bool
    training_losses: dict[str, float]  # e.g. { "epoch_1": 0.5, "epoch_2": 0.3 }

Now, let’s see how the ClientApp can serialize this object, send it to the ServerApp, make the strategy deserialize it back to the original object, and use it.

Sending from ClientApps

Let’s assume our ClientApp trains the model locally and generates an instance of TrainProcessMetadata. In order to send it as part of the message reply, we need to serialize it to bytes. In this case, we can use the pickle module from the Python standard library. We can then send the serialized object in a ConfigRecord in the Message reply. Let’s see how this would look like in code:

경고

The following code is for demonstration purposes only. In real-world applications, since pickle can execute arbitrary code during unpickling, you should use a SAFE serialization method than pickle, such as json or a simple custom solution if the object is not too complex. pickle is used here solely for simplicity.

import pickle


@app.train()
def train(msg: Message, context: Context):
    """Train the model on local data."""

    # ... prepare model, load data, train locally
    # The train function returns a TrainProcessMetadata object
    train_metadata = train_fn(...)
    # For example:

    # TrainProcessMetadata(
    #     training_time=123.45,
    #     converged=True,
    #     training_losses={"epoch1": 0.56, "epoch2": 0.34}
    # )

    # Serialize the TrainProcessMetadata object to bytes
    train_meta_bytes = pickle.dumps(train_metadata)
    # Construct a ConfigRecord
    config_record = ConfigRecord({"meta": train_meta_bytes})

    # Construct and return reply Message
    model_record = ArrayRecord(model.state_dict())
    metrics = {
        "train_loss": train_loss,
        "num-examples": len(trainloader.dataset),
    }
    metric_record = MetricRecord(metrics)
    content = RecordDict(
        {
            "arrays": model_record,
            "metrics": metric_record,
            "train_metadata": config_record,
        }
    )
    return Message(content=content, reply_to=msg)

Let’s see next how the strategy on the ServerApp can deserialize the object back to its original form and use it.

Receiving on ServerApps

As you know, a Flower strategy will automatically aggregate the ArrayRecord and MetricRecord from each client. However, it will not do anything with the ConfigRecord we just sent. We can override the aggregate_train method of our strategy to handle the deserialization and use of the TrainProcessMetadata object.

참고

We override the aggregate_train method because we sent the object from a @app.train() function. If we had sent it from an @app.evaluate() function, we would override the aggregate_evaluate method instead.

Let’s create a new custom strategy (or reuse the one created in part 2 and part 3 of this tutorial) in server_app.py that extends the FedAdagrad strategy and overrides the aggregate_train method to deserialize the TrainProcessMetadata object from each client and print the training time and convergence status:

import pickle
from dataclasses import asdict
from typing import Iterable, Optional


class CustomFedAdagrad(FedAdagrad):

    def aggregate_train(
        self,
        server_round: int,
        replies: Iterable[Message],
    ) -> tuple[Optional[ArrayRecord], Optional[MetricRecord]]:
        """Aggregate ArrayRecords and MetricRecords in the received Messages."""

        for reply in replies:
            if reply.has_content():
                # Retrieve the ConfigRecord from the message
                config_record = reply.content["train_metadata"]
                metadata_bytes = config_record["meta"]
                # Deserialize it
                train_meta = pickle.loads(metadata_bytes)
                print(asdict(train_meta))
        # Aggregate the ArrayRecords and MetricRecords as usual
        return super().aggregate_train(server_round, replies)

Finally, we run the Flower App.

$ flwr run .

You will observe that the training metadata from each client is logged to the console of the ServerApp. If you finish embedding the creation of the TrainProcessMetadata object in the ClientApp, you should see output similar to this:

INFO :      [ROUND 1/3]
INFO :      configure_train: Sampled 25 nodes (out of 1000)
{'training_time': 123.45, 'converged': True, 'training_losses': {'epoch1': 0.56, 'epoch2': 0.34}}
{'training_time': 130.67, 'converged': False, 'training_losses': {'epoch1': 0.60, 'epoch2': 0.40}}
...

You can now use this information in your strategy logic as needed. For example, to implement a custom aggregation method based on convergence status or to log additional metrics.

Recap

In this part of the tutorial, we’ve seen how to communicate arbitrary Python objects between the ClientApp and the ServerApp by serializing them to bytes and sending them as a ConfigRecord in a Message. We also learned how to deserialize them back to their original form on the server side and use them in a custom strategy. Note that the steps presented here are identical if you need to serialize objects in the strategy to send them to the clients.

다음 단계

Before you continue, make sure to join the Flower community on Flower Discuss (Join Flower Discuss) and on Slack (Join Slack).

There’s a dedicated #questions Slack channel if you need help, but we’d also love to hear who you are in #introductions!

This is the final part of the Flower tutorial (for now!), congratulations! You’re now well equipped to understand the rest of the documentation. There are many topics we didn’t cover in the tutorial, we recommend the following resources:

• Read Flower Docs

• Check out Flower Code Examples

• Use Flower Baselines for your research

• Watch Flower AI Summit 2025 videos