de març 06, 2019 —
Posted by Alex Ingerman (Product Manager) and Krzys Ostrowski (Research Scientist)
There are an estimated 3 billion smartphones in the world, and 7 billion connected devices. These phones and devices are constantly generating new data. Traditional analytics and machine learning need that data to be centrally collected before it is processed to yield insights, ML models and ultimately better produ…
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TensorFlow Federated enables developers to express and simulate federated learning systems. Pictured here, each phone trains the model locally (A). Their updates are aggregated (B) to form an improved shared model (C). |
# Load simulation data.
source, _ = tff.simulation.datasets.emnist.load_data()
def client_data(n):
dataset = source.create_tf_dataset_for_client(source.client_ids[n])
return mnist.keras_dataset_from_emnist(dataset).repeat(10).batch(20)
# Wrap a Keras model for use with TFF.
def model_fn():
return tff.learning.from_compiled_keras_model(
mnist.create_simple_keras_model(), sample_batch)
# Simulate a few rounds of training with the selected client devices.
trainer = tff.learning.build_federated_averaging_process(model_fn)
state = trainer.initialize()
for _ in range(5):
state, metrics = trainer.next(state, train_data)
print (metrics.loss)
You can see the rest in the federated MNIST classifications tutorial.tf.float32
) and where that data lives (on distributed clients).READINGS_TYPE = tff.FederatedType(tf.float32, tff.CLIENTS)
And then specify a federated average function over that type.@tff.federated_computation(READINGS_TYPE)
def get_average_temperature(sensor_readings):
return tff.federated_average(sensor_readings)
After the federated computation is defined, TFF represents it in a form that could be run in a decentralized setting. TFF’s initial release includes a local-machine runtime that simulates the computation being executed across a set of clients holding the data, with each client computing their local contribution, and the centralized coordinator aggregating all the contributions. From the developer’s perspective, though, the federated computation can be seen as an ordinary function, that happens to have inputs and outputs that reside in different places (on individual clients and in the coordinating service, respectively).![]() |
An illustration of the get_average_temperature federated computation expression. |
@tff.federated_computation(
tff.FederatedType(DATASET_TYPE, tff.CLIENTS),
tff.FederatedType(MODEL_TYPE, tff.SERVER, all_equal=True),
tff.FederatedType(tf.float32, tff.SERVER, all_equal=True))
def federated_train(client_data, server_model, learning_rate):
return tff.federated_average(
tff.federated_map(local_train, [
client_data,
tff.federated_broadcast(server_model),
tff.federated_broadcast(learning_rate)]))
With TensorFlow Federated, we are taking a step towards making the technology accessible to a wider audience, and inviting community participation in developing federated learning research on top of an open, flexible platform. You can try out TFF in your browser, with just a few clicks, by walking through the tutorials. There are many ways to get involved: you can experiment with existing FL algorithms on your models, contribute new federated datasets and models to the TFF repository, add implementations of new FL algorithms, or extend existing ones with new features.
de març 06, 2019
—
Posted by Alex Ingerman (Product Manager) and Krzys Ostrowski (Research Scientist)
There are an estimated 3 billion smartphones in the world, and 7 billion connected devices. These phones and devices are constantly generating new data. Traditional analytics and machine learning need that data to be centrally collected before it is processed to yield insights, ML models and ultimately better produ…