Design stateful ClientApps¶

By design, ClientApp objects are stateless. This means that the ClientApp object is recreated each time a new Message is to be processed. This behavior is identical with Flower’s Simulation Engine and Deployment Engine. For the former, it allows us to simulate the running of a large number of nodes on a single machine or across multiple machines. For the latter, it enables each SuperNode to be part of multiple runs, each running a different ClientApp.

When a ClientApp is executed it receives a Context. This context is unique for each ClientApp, meaning that subsequent executions of the same ClientApp from the same node will receive the same Context object. In the Context, the .state attribute (of type RecordDict) can be used to store information that you would like the ClientApp to have access to for the duration of the run. This could be anything from intermediate results such as the history of training losses (e.g. as a list of float values with a new entry appended each time the ClientApp is executed), certain parts of the model that should persist on the client side, or some other arbitrary Python objects. These items would need to be serialized before saving them into the context.

Saving metrics to the context¶

This section will demonstrate how to save metrics such as accuracy/loss values to the Context so they can be used in subsequent executions of the ClientApp.

Let’s begin with a simple setting in which ClientApp is defined as follows. The train() function only generates a random number, prints it, and return an empty message.

Tip

You can create a PyTorch project with ready-to-use ClientApp and other components by running flwr new.

import random
from flwr.app import Context, Message, RecordDict
from flwr.clientapp import ClientApp

# Flower ClientApp
app = ClientApp()


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

    # Generate a random integer between 0 and 10
    n = random.randint(0, 10)
    print(n)
    return Message(RecordDict(), reply_to=msg)

With the minimal ClientApp above, each time a Message is addressed to this train function, a new random integer will be generated and printed. Let’s say we want to save that randomly generated integer and append it to a list that persists in the Context. This way, each time the function executes,, it prints the history of random integers. Let’s see how this looks in code:

Tip

Recall, the state attribute of a Context object is of type RecordDict, which is a special dictionary for different types of records available in Flower. This means that you can save to it not just MetricRecord as in the example below, but also ArrayRecord and ConfigRecord objects.

import random
from flwr.app import Context, Message, RecordDict
from flwr.clientapp import ClientApp

# Flower ClientApp
app = ClientApp()


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

    # Generate a random integer between 0 and 10
    n = random.randint(0, 10)
    print(n)

    # Append to list in context or initialize if it doesn't exist
    if "random-metrics" not in context.state:
        # Initialize MetricRecord in state
        context.state["random-metrics"] = MetricRecord({"random-ints": []})

    # Append to record
    context.state["random-metrics"]["random-ints"].append(n)

    # Print history
    print(context.state["random-metrics"])
    return Message(RecordDict(), reply_to=msg)

If you run a Flower App including the above logic in your ClientApp and having just two clients in your federation sampled in each round, you’ll see an output similar to the one below. See how after each round the random-metrics record in the Context gets one additional integer? Note that, in Simulation Runtime, the order of log messages may change each round due to the random ordering of simulated clients.

# round 1
config_records={'random-metrics': {'random-ints': [2]}}
config_records={'random-metrics': {'random-ints': [7]}}

# round 2
config_records={'random-metrics': {'random-ints': [2, 5]}}
config_records={'random-metrics': {'random-ints': [7, 4]}}

# round 3
config_records={'random-metrics': {'random-ints': [2, 5, 1]}}
config_records={'random-metrics': {'random-ints': [7, 4, 2]}}

Saving model parameters to the context¶

Using ConfigRecord or MetricRecord to save “simple” components is fine (e.g., float, integer, boolean, string, bytes, and lists of these types. Note that MetricRecord only supports float, integer, and lists of these types). Flower has a specific type of record, an ArrayRecord, for storing model parameters, or more generally, data arrays.

Let’s see a couple of examples of how to save NumPy arrays first and then how to save parameters of PyTorch and TensorFlow models.

Note

The examples below omit the definition of a ClientApp to keep the code blocks concise. To make use of ArrayRecord objects in your ClientApp you can follow the same principles as outlined earlier.

Saving NumPy arrays to the context¶

Elements stored in an ArrayRecord are of type Array, which is a data structure that holds bytes and metadata that can be used for deserialization. Let’s see how to create an Array from a NumPy array and insert it into an ArrayRecord.

Note

Array objects carry bytes as their main payload and additional metadata to use for deserialization. You can also implement your own serialization/deserialization.

Let’s see how to use those functions to store a NumPy array into the context.

import numpy as np
from flwr.app import Array, ArrayRecord, Context


# Let's create a simple NumPy array
arr_np = np.random.randn(3, 3)

# If we print it
# array([[-1.84242409, -1.01539537, -0.46528405],
#        [ 0.32991896,  0.55540414,  0.44085534],
#        [-0.10758364,  1.97619858, -0.37120501]])

# Now, let's serialize it and construct an Array
arr = Array(arr_np)

# If we print it (note the binary data)
# Array(dtype='float64', shape=[3, 3], stype='numpy.ndarray', data=b'\x93NUMPY\x01\x00v\x00...)

# It can be inserted in an ArrayRecord like this
arr_record = ArrayRecord()
arr_record["my_array"] = arr
# You can also do it via the constructor
# arr_record = ArrayRecord({"my_array": arr})

# If you don't need the keys, you can also pass a list of Numpy arrays
# arr_record = ArrayRecord([arr_np])

# Then, it can be added to the state in the context
context.state["some_parameters"] = arr_record

To extract the data in an ArrayRecord, you just need to deserialize the array of interest. For example, following the example above:

# Get Array from context
arr = context.state["some_parameters"]["my_array"]

# If you constructed the ArrayRecord with a list of Numpy, then do
# arr = context.state["some_parameters"].to_numpy_ndarrays()[0]  # get first array

# Deserialize it
arr_deserialized = arr.numpy()

# If we print it (it should show the exact same values as earlier)
# array([[-1.84242409, -1.01539537, -0.46528405],
#        [ 0.32991896,  0.55540414,  0.44085534],
#        [-0.10758364,  1.97619858, -0.37120501]])

Saving PyTorch parameters to the context¶

Flower offers one-liner utilities to convert PyTorch model parameters to/from ArrayRecord objects. Let’s see how to do that.

import torch
import torch.nn as nn
import torch.nn.functional as F
from flwr.app import ArrayRecord


class Net(nn.Module):
    """A very simple model"""

    def __init__(self):
        super().__init__()
        self.conv = nn.Conv2d(3, 32, 5)
        self.fc = nn.Linear(1024, 10)

    def forward(self, x):
        x = F.relu(self.conv(x))
        return self.fc(x)


# Instantiate model as usual
model = Net()

# Save the state_dict into a single ArrayRecord
arr_record = ArrayRecord(model.state_dict())

# Add to a context
context.state["net_parameters"] = arr_record

Let’s say now you want to apply the parameters stored in your context to a new instance of the model (as it happens each time a ClientApp is executed). You will need to:

  1. Retrieve the ArrayRecord from the context

  2. Construct a state_dict and load it

state_dict = {}
# Extract record from context
arr_record = context.state["net_parameters"]

# Deserialize the parameters
state_dict = arr_record.to_torch_state_dict()

# Apply state dict to a new model instance
model_ = Net()
model_.load_state_dict(state_dict)
# now this model has the exact same parameters as the one created earlier
# You can verify this by doing
for p, p_ in zip(model.state_dict().values(), model_.state_dict().values()):
    assert torch.allclose(p, p_), "`state_dict`s do not match"

And that’s it! Recall that even though this example shows how to store the entire state_dict in an ArrayRecord, you can just save part of it. The process would be identical, but you might need to adjust how it is loaded into an existing model using PyTorch APIs.

Saving Tensorflow/Keras parameters to the context¶

Follow the same steps as done above but replace the state_dict logic with simply get_weights() to convert the model parameters to a list of NumPy arrays that can then be saved into an ArrayRecord. Then, after deserialization, use set_weights() to apply the new parameters to a model.

import tensorflow as tf
from flwr.app import ArrayRecord

# Define a simple model
model = tf.keras.Sequential(
    [
        tf.keras.layers.Flatten(input_shape=(28, 28)),
        tf.keras.layers.Dense(128, activation="relu"),
        tf.keras.layers.Dense(10),
    ]
)

# Save model weights into an ArrayRecord and add to a context
context.state["model_weights"] = ArrayRecord(model.get_weights())

...

# Extract record from context and apply to the model
model.set_weights(context.state["model_weights"].to_numpy_ndarrays())