Category "tensorflow-federated"

TFF : Print state that receives each client

I would like to print the state that receives each client after training, Here is my loop: NUM_ROUNDS = 5 for round_num in range(NUM_ROUNDS): print('Round {r}

The output of : create_tf_dataset_from_all_clients

In order to understand the output of .create_tf_dataset_from_all_clients, and like the official link say : Creates a new tf.data.Dataset containing all client

TFF: 'trainable=True ' causes decrinsing of accuracy

I work with TFF, here is a part of my code : def create_keras_model(): baseModel = tf.keras.applications.ResNet50(include_top=False, weights=None, inpu

TFF: 'trainable=True ' causes decrinsing of accuracy

I work with TFF, here is a part of my code : def create_keras_model(): baseModel = tf.keras.applications.ResNet50(include_top=False, weights=None, inpu

in TFF context : Is the evaluation step depends on training process?

We all know that the evaluation step is quite important to evaluate our model on a test basis. I wanted to know if it is necessary to go through the round step

How to create federated dataset from a CSV file?

I have selected this dataset: https://www.kaggle.com/karangadiya/fifa19 Now, I would like to convert this CSV file into the federated dataset to fit in the mod

Missing required positional argument:

I tried to implement federated learning based on the LSTM approach. def create_keras_model(): model = Sequential() model.add(LSTM(32, input_shape=(3,1))

Use different metrics in tf.keras.metrics for mutli-classification model

I am using the TensorFlow federated framework for a multiclassification problem. I am following the tutorials and most of them use the metric (tf.keras.metrics.

Unable to install 'Tensorflow Federated' on Apple Silicon M1

I have TensorFlow (2.8.0) installed and running on my Apple Silicon M1 MacBook. But facing dependency error on trying to install tensorflow-federated with the b

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