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
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
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
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
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
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))
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.
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