basic model federated implementation

src/participation-hub/federated-learning-microservice/app/federatedDataImplementation.py

+import collections
+import attr
+import functools
+import numpy as np
+import tensorflow as tf
+import tensorflow_federated as tff
+import time
+
+np.random.seed(0)
+
+client_data, _ = tff.simulation.datasets.emnist.load_data()
+
+first_client_id = client_data.client_ids[0]
+first_client_dataset = client_data.create_tf_dataset_for_client(
+    first_client_id)
+print(first_client_dataset.element_spec)
+# This information is also available as a `ClientData` property:
+assert client_data.element_type_structure == first_client_dataset.element_spec
+
+def preprocess_dataset(dataset):
+  """Create batches of 5 examples, and limit to 3 batches."""
+
+  def map_fn(input):
+    return collections.OrderedDict(
+        x=tf.reshape(input['pixels'], shape=(-1, 784)),
+        y=tf.cast(tf.reshape(input['label'], shape=(-1, 1)), tf.int64),
+    )
+
+  return dataset.batch(5).map(
+      map_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE).take(5)
+
+
+preprocessed_client_data = client_data.preprocess(preprocess_dataset)
+
+#Notice that we have both reshaped and renamed the elements of the ordered dict.
+# first_client_dataset = preprocessed_client_data.create_tf_dataset_for_client(
+#     first_client_id)
+# print(first_client_dataset.element_spec)
+
+def preprocess_and_shuffle(dataset):
+  """Applies `preprocess_dataset` above and shuffles the result."""
+  preprocessed = preprocess_dataset(dataset)
+  return preprocessed.shuffle(buffer_size=5)
+
+preprocessed_and_shuffled = client_data.preprocess(preprocess_and_shuffle)
+
+# The type signature will remain the same, but the batches will be shuffled.
+first_client_dataset = preprocessed_and_shuffled.create_tf_dataset_for_client(
+    first_client_id)
+print(first_client_dataset.element_spec)
+
+###################################################
+
+def model_fn():
+  model = tf.keras.models.Sequential([
+      tf.keras.layers.InputLayer(input_shape=(784,)),
+      tf.keras.layers.Dense(10, kernel_initializer='zeros'),
+  ])
+  return tff.learning.from_keras_model(
+      model,
+      # Note: input spec is the _batched_ shape, and includes the 
+      # label tensor which will be passed to the loss function. This model is
+      # therefore configured to accept data _after_ it has been preprocessed.
+      input_spec= first_client_dataset.element_spec,
+        #   collections.OrderedDict(
+        #       x=tf.TensorSpec(shape=[None, 784], dtype=tf.float32),
+        #       y=tf.TensorSpec(shape=[None, 1], dtype=tf.int64)),
+      loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+      metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
+
+trainer = tff.learning.build_federated_averaging_process(
+    model_fn,
+    client_optimizer_fn=lambda: tf.keras.optimizers.SGD(learning_rate=0.01))
+
+selected_client_ids = preprocessed_and_shuffled.client_ids[:10]
+print(str(selected_client_ids))
+
+preprocessed_data_for_clients = [
+    preprocessed_and_shuffled.create_tf_dataset_for_client(
+        selected_client_ids[i]) for i in range(10)
+]
+
+state = trainer.initialize()
+for _ in range(5):
+  t1 = time.time()
+  state, metrics = trainer.next(state, preprocessed_data_for_clients)
+  t2 = time.time()
+  print('loss {}, round time {}'.format(metrics['train']['loss'], t2 - t1))
+
+print('dataset computation without preprocessing:')
+#print(client_data.dataset_computation.type_signature)
+print('\n')
+print('dataset computation with preprocessing:')
+print(preprocessed_and_shuffled.dataset_computation.type_signature)
+
+trainer_accepting_ids = tff.simulation.compose_dataset_computation_with_iterative_process(
+    preprocessed_and_shuffled.dataset_computation, trainer)
+
+for _ in range(5):
+  t1 = time.time()
+  state, metrics = trainer_accepting_ids.next(state, selected_client_ids)
+  t2 = time.time()
+  print('loss {}, round time {}'.format(metrics['train']['loss'], t2 - t1))

src/participation-hub/federated-learning-microservice/app/implementationLog

+## Starting...
+## Preprocessing the federated_train_data
+## Declaring the model
+## Declaring the federated algorithm
+tf_dataset_type
+<x=float32[?,784],y=int32[?,1]>*
+model_weights_type
+<float32[784,10],float32[10]>
+federated_algorithm.initialize.type_signature
+( -> <float32[784,10],float32[10]>@SERVER)
+federated_algorithm.next.type_signature
+(<server_weights=<float32[784,10],float32[10]>@SERVER,federated_dataset={<x=float32[?,784],y=int32[?,1]>*}@CLIENTS> -> <float32[784,10],float32[10]>@SERVER)
+## Training the model
+round  1, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.9843163), ('accuracy', 0.086021505)]))])
+round  2, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.9829092), ('accuracy', 0.088954054)]))])
+round  3, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.9649642), ('accuracy', 0.092864126)]))])
+round  4, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.937074), ('accuracy', 0.10068426)]))])
+round  5, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.9075902), ('accuracy', 0.10166178)]))])
+round  6, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.8781416), ('accuracy', 0.10459433)]))])
+round  7, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.8490415), ('accuracy', 0.10557185)]))])
+round  8, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.8203006), ('accuracy', 0.10752688)]))])
+round  9, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.791923), ('accuracy', 0.11045943)]))])
+round 10, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.7639053), ('accuracy', 0.11339199)]))])
+## Evaluation of the model
+## Starting...
+## Preprocessing the federated_train_data
+## Declaring the model
+## Declaring the federated algorithm
+tf_dataset_type
+<x=float32[?,784],y=int32[?,1]>*
+model_weights_type
+<float32[784,10],float32[10]>
+federated_algorithm.initialize.type_signature
+( -> <float32[784,10],float32[10]>@SERVER)
+federated_algorithm.next.type_signature
+(<server_weights=<float32[784,10],float32[10]>@SERVER,federated_dataset={<x=float32[?,784],y=int32[?,1]>*}@CLIENTS> -> <float32[784,10],float32[10]>@SERVER)
+## Training the model
+round  1, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.9843163), ('accuracy', 0.086021505)]))])
+round  2, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.982909), ('accuracy', 0.088954054)]))])
+round  3, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.9649642), ('accuracy', 0.092864126)]))])
+round  4, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.937074), ('accuracy', 0.10068426)]))])
+round  5, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.9075902), ('accuracy', 0.10166178)]))])
+round  6, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.8781416), ('accuracy', 0.10459433)]))])
+round  7, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.849042), ('accuracy', 0.10557185)]))])
+round  8, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.8203006), ('accuracy', 0.10752688)]))])
+round  9, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.7919228), ('accuracy', 0.11045943)]))])
+round 10, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.763905), ('accuracy', 0.11339199)]))])
+## Evaluation of the model
+## Starting...
+## Preprocessing the federated_train_data
+## Declaring the model
+## Declaring the federated algorithm
+tf_dataset_type
+<x=float32[?,784],y=int32[?,1]>*
+model_weights_type
+<float32[784,10],float32[10]>
+federated_algorithm.initialize.type_signature
+( -> <float32[784,10],float32[10]>@SERVER)
+federated_algorithm.next.type_signature
+(<server_weights=<float32[784,10],float32[10]>@SERVER,federated_dataset={<x=float32[?,784],y=int32[?,1]>*}@CLIENTS> -> <float32[784,10],float32[10]>@SERVER)
+## Training the model
+round  1, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.9843163), ('accuracy', 0.086021505)]))])
+round  2, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.982909), ('accuracy', 0.088954054)]))])
+round  3, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.9649642), ('accuracy', 0.092864126)]))])
+round  4, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.937074), ('accuracy', 0.10068426)]))])
+round  5, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.9075902), ('accuracy', 0.10166178)]))])
+round  6, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.8781419), ('accuracy', 0.10459433)]))])
+round  7, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.8490417), ('accuracy', 0.10557185)]))])
+round  8, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.8203006), ('accuracy', 0.10752688)]))])
+round  9, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.7919226), ('accuracy', 0.11045943)]))])
+round 10, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.7639048), ('accuracy', 0.11339199)]))])
+## Evaluation of the model
+## Starting...
+## Preprocessing the federated_train_data
+## Declaring the model
+## Declaring the federated algorithm
+## Training the model
+round  1, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.9843166), ('accuracy', 0.086021505)]))])
+round  2, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.9829092), ('accuracy', 0.088954054)]))])
+round  3, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.964964), ('accuracy', 0.092864126)]))])
+round  4, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.9370737), ('accuracy', 0.10068426)]))])
+round  5, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.90759), ('accuracy', 0.10166178)]))])
+round  6, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.8781416), ('accuracy', 0.10459433)]))])
+round  7, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.8490417), ('accuracy', 0.10557185)]))])
+round  8, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.8203006), ('accuracy', 0.10752688)]))])
+round  9, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.7919228), ('accuracy', 0.11045943)]))])
+round 10, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('value_sum_process', ()), ('weight_sum_process', ())])), ('train', OrderedDict([('num_examples', 1023.0), ('loss', 2.763905), ('accuracy', 0.11339199)]))])
+## Evaluation of the model
+## Starting...
+## Preprocessing the federated_train_data
+## Declaring the model
+## Declaring the federated algorithm
+tf_dataset_type
+<x=float32[?,784],y=int32[?,1]>*
+model_weights_type
+<float32[784,10],float32[10]>
+FINISHEEED
+## Evaluation of the model
+## Starting...
+## Preprocessing the federated_train_data
+## Declaring the model
+## Declaring the federated algorithm
+tf_dataset_type
+<x=float32[?,784],y=int32[?,1]>*
+model_weights_type
+<float32[784,10],float32[10]>
+FINISHEEED
+## Evaluation of the model
+mean_client_wieghts
+<float32[784,10],float32[10]>@SERVER
+mean_client_wieghts
+<float32[784,10],float32[10]>@SERVER
+## Starting...
+## Preprocessing the federated_train_data
+## Declaring the model
+## Declaring the federated algorithm
+tf_dataset_type
+<x=float32[?,784],y=int32[?,1]>*
+model_weights_type
+<float32[784,10],float32[10]>
+FINISHEEED
+## Evaluation of the model
+server_weights
+<float32[784,10],float32[10]>@SERVER
+mean_client_wieghts
+<float32[784,10],float32[10]>@SERVER
+## Starting...
+## Preprocessing the federated_train_data
+## Declaring the model
+## Declaring the federated algorithm
+tf_dataset_type
+<x=float32[?,784],y=int32[?,1]>*
+model_weights_type
+<float32[784,10],float32[10]>
+FINISHEEED
+## Evaluation of the model
+server_weights
+<float32[784,10],float32[10]>@SERVER
+mean_client_wieghts
+<float32[784,10],float32[10]>@SERVER
+## Starting...
+## Preprocessing the federated_train_data
+## Declaring the model
+## Declaring the federated algorithm
+tf_dataset_type
+<x=float32[?,784],y=int32[?,1]>*
+model_weights_type
+<float32[784,10],float32[10]>
+server_state
+server_weights
+<float32[784,10],float32[10]>@SERVER
+mean_client_wieghts
+<float32[784,10],float32[10]>@SERVER
+## Starting...
+## Preprocessing the federated_train_data
+## Declaring the model
+## Declaring the federated algorithm
+tf_dataset_type
+<x=float32[?,784],y=int32[?,1]>*
+model_weights_type
+<float32[784,10],float32[10]>
+server_state

src/participation-hub/federated-learning-microservice/app/implementationScript.sh

+#! /bin/bash
+
+#source venv/bin/activate
+
+python processing/main_processing.py >> implementationLog

src/participation-hub/federated-learning-microservice/app/mylog

+Submitted batch job 1721
+Submitted batch job 1722
+Submitted batch job 1723
+Submitted batch job 1724
+Submitted batch job 1725
+Submitted batch job 1726
+Submitted batch job 1727
+Submitted batch job 1728
+Submitted batch job 1729
+Submitted batch job 1730
+Submitted batch job 1731
+Submitted batch job 1732
+Submitted batch job 1733
+Submitted batch job 1734
+Submitted batch job 1735
+Submitted batch job 1736
+Submitted batch job 1737
+Submitted batch job 1738
+Submitted batch job 1739
+Submitted batch job 1740
+Submitted batch job 1741
+Submitted batch job 1742
+Submitted batch job 1743
+Submitted batch job 1744
+Submitted batch job 1745
+Submitted batch job 1746
+Submitted batch job 1747
+Submitted batch job 1748
+Submitted batch job 1749
+Submitted batch job 1750
+Submitted batch job 1751
+Submitted batch job 1752
+Submitted batch job 1753
+Submitted batch job 1754
+Submitted batch job 1755
+Submitted batch job 1756
+Submitted batch job 1757
+Submitted batch job 1758
+Submitted batch job 1759
+Submitted batch job 1760
+Submitted batch job 1761
+Submitted batch job 1762
+Submitted batch job 1763
+Submitted batch job 1764
+Submitted batch job 1765
+Submitted batch job 1766
+Submitted batch job 1767
+Submitted batch job 1768
+Submitted batch job 1769
+Submitted batch job 1777
+Submitted batch job 1778
+Submitted batch job 1779

src/participation-hub/federated-learning-microservice/app/mylogsource

+Submitted batch job 1770
+Submitted batch job 1771
+Submitted batch job 1772
+Submitted batch job 1773
+Submitted batch job 1774
+Submitted batch job 1775
+Submitted batch job 1776

src/participation-hub/federated-learning-microservice/app/processing/evaluation.py

+import numpy as np
+import tensorflow as tf
+import tensorflow_federated as tff
+from models import MnistModel
+
+
+def evaluate(server_state):
+  keras_model = MnistModel()
+  keras_model.compile(
+      loss=tf.keras.losses.SparseCategoricalCrossentropy(),
+      metrics=[tf.keras.metrics.SparseCategoricalAccuracy()]  
+  )
+  keras_model.set_weights(server_state)
+  keras_model.evaluate(central_emnist_test)
+
+
+def evaluate2(server_state,tff_learning_model):
+  
+  #TODO: Assign weights to the model
+  #First idea = server_update function??
+
+  evaluation = tff.learning.build_federated_evaluation(tff_learning_model)
+  
+  
+  # keras_model = MnistModel()
+  # keras_model.compile(
+  #     loss=tf.keras.losses.SparseCategoricalCrossentropy(),
+  #     metrics=[tf.keras.metrics.SparseCategoricalAccuracy()]  
+  # )
+  # keras_model.set_weights(server_state)
+  # keras_model.evaluate(central_emnist_test)

src/participation-hub/federated-learning-microservice/app/processing/federated_training_algorithm.py

+import numpy as np
+import tensorflow as tf
+import tensorflow_federated as tff
+from models import MnistModel
+
+#tf_dataset_type = None
+#model_weights_type = None
+
+@tf.function
+def client_update(model, dataset, server_weights, client_optimizer):
+  """Performs training (using the server model weights) on the client's dataset."""
+  # Initialize the client model with the current server weights.
+  client_weights = model.trainable_variables
+  # Assign the server weights to the client model.
+  tf.nest.map_structure(lambda x, y: x.assign(y),
+                        client_weights, server_weights)
+
+  # Use the client_optimizer to update the local model.
+  for batch in dataset:
+    with tf.GradientTape() as tape:
+      # Compute a forward pass on the batch of data
+      outputs = model.forward_pass(batch)
+
+    # Compute the corresponding gradient
+    grads = tape.gradient(outputs.loss, client_weights)
+    grads_and_vars = zip(grads, client_weights)
+
+    # Apply the gradient using a client optimizer.
+    client_optimizer.apply_gradients(grads_and_vars)
+
+  return client_weights
+
+@tf.function
+def server_update(model, mean_client_weights):
+  """Updates the server model weights as the average of the client model weights."""
+  model_weights = model.trainable_variables
+  # Assign the mean client weights to the server model.
+  tf.nest.map_structure(lambda x, y: x.assign(y),
+                        model_weights, mean_client_weights)
+  return model_weights
+
+
+
+
+#Creating the initialization computation
+@tff.tf_computation
+def server_init():
+  model = MnistModel() #model_fn()
+  return model.trainable_variables
+
+@tff.federated_computation
+def initialize_fn():
+  return tff.federated_value(server_init(), tff.SERVER)
+
+whimsy_model = MnistModel()
+tf_dataset_type = tff.SequenceType(whimsy_model.input_spec)
+model_weights_type = server_init.type_signature.result
+
+@tff.tf_computation(tf_dataset_type, model_weights_type)
+def client_update_fn(tf_dataset, server_weights):
+  model = MnistModel()#model_fn()
+  client_optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)
+  return client_update(model, tf_dataset, server_weights, client_optimizer)
+
+@tff.tf_computation(model_weights_type)
+def server_update_fn(mean_client_weights):
+  model = MnistModel()#model_fn()
+  return server_update(model, mean_client_weights)
+
+federated_server_type = tff.FederatedType(model_weights_type, tff.SERVER)
+federated_dataset_type = tff.FederatedType(tf_dataset_type, tff.CLIENTS)
+
+@tff.federated_computation(federated_server_type, federated_dataset_type)
+def next_fn(server_weights, federated_dataset):
+  # Broadcast the server weights to the clients.
+  print("server_weights")
+  print(str(server_weights.type_signature))
+  server_weights_at_client = tff.federated_broadcast(server_weights)
+
+  # Each client computes their updated weights.
+  client_weights = tff.federated_map(
+      client_update_fn, (federated_dataset, server_weights_at_client))
+
+  # The server averages these updates.
+  mean_client_weights = tff.federated_mean(client_weights)
+
+  print("mean_client_wieghts")
+  print(str(mean_client_weights.type_signature))
+
+  # The server updates its model.
+  server_weights = tff.federated_map(server_update_fn, mean_client_weights)
+
+  return server_weights
+
+def get_federated_algorithm():
+    #Creating the next_fn
+
+    #Getting the data type, needed explicitily in the modell functions
+    whimsy_model = MnistModel() #model_fn()
+    global tf_dataset_type
+    tf_dataset_type = tff.SequenceType(whimsy_model.input_spec)
+    print("tf_dataset_type")
+    print(str(tf_dataset_type))
+    global model_weights_type
+    model_weights_type = server_init.type_signature.result
+    print("model_weights_type")
+    print(str(model_weights_type))
+    # finished printing types
+
+    federated_server_type = tff.FederatedType(model_weights_type, tff.SERVER)
+    federated_dataset_type = tff.FederatedType(tf_dataset_type, tff.CLIENTS)
+
+    federated_algorithm = tff.templates.IterativeProcess(
+        initialize_fn=initialize_fn,
+        next_fn=next_fn
+    )
+    return federated_algorithm
+
+
+
+def merge_2_states(server_state1, server_state2):
+  return np.mean( np.array([ server_state, server2_state ]), axis=0 )
\ No newline at end of file

src/participation-hub/federated-learning-microservice/app/processing/main_processing.py

+import collections
+import attr
+import functools
+import numpy as np
+import tensorflow as tf
+import tensorflow_federated as tff
+from preprocessing import get_client_ids
+from preprocessing import get_federated_train_data
+from preprocessing import preprocess
+from models import MnistModel
+from federated_training_algorithm import get_federated_algorithm
+from federated_training_algorithm import merge_2_states
+from evaluation import evaluate
+
+np.random.seed(0)
+print("## Starting...")
+
+#GET THE DATA (for now it's the default dataset)
+emnist_train, emnist_test = tff.simulation.datasets.emnist.load_data()
+
+######client_ids = get_client_ids(emnist_train) not used
+print("## Preprocessing the federated_train_data")
+federated_train_data = get_federated_train_data(emnist_train)
+
+print("## Declaring the model")
+#it is done in models.py
+print("## Declaring the federated algorithm")
+
+federated_algorithm = get_federated_algorithm()
+
+server_state = federated_algorithm.initialize()
+
+for round in range(20):
+  server_state = federated_algorithm.next(server_state, federated_train_data)
+
+print("server_state type")
+print(str(type(server_state)))
+print(str(type(server_state[0])))
+
+print("FINISHEEED")
+
+server2_state = federated_algorithm.initialize()
+for round in range(2):
+  server2_state = federated_algorithm.next(server2_state, federated_train_data)
+
+
+
+
+print("server_state[1]")
+print(server_state[1])
+
+print("server2_state[1]")
+print(server2_state[1])
+
+print("merged_state[1]")
+print(merged_state[1])
+
+
+
+
+# print("federated_algorithm.initialize.type_signature")
+# print(str(federated_algorithm.initialize.type_signature)
+# print("federated_algorithm.next.type_signature")
+# print(str(federated_algorithm.next.type_signature))
+
+# print("## Training the model")
+# iterative_process = tff.learning.build_federated_averaging_process(
+#     MnistModel,
+#     client_optimizer_fn=lambda: tf.keras.optimizers.SGD(learning_rate=0.02))
+
+# state = iterative_process.initialize()
+
+# for round_num in range(1, 11):
+#   state, metrics = iterative_process.next(state, federated_train_data)
+#   print('round {:2d}, metrics={}'.format(round_num, metrics))
+#evaluation = tff.learning.build_federated_evaluation(MnistModel)
+
+#TODO integration
+print("## Evaluation of the model")
\ No newline at end of file

src/participation-hub/federated-learning-microservice/app/processing/models.py

+import numpy as np
+import tensorflow as tf
+import tensorflow_federated as tff
+import collections
+
+input_spec_data_global = None
+
+MnistVariables = collections.namedtuple(
+    'MnistVariables', 'weights bias num_examples loss_sum accuracy_sum')
+
+def create_mnist_variables():
+  return MnistVariables(
+      weights=tf.Variable(
+          lambda: tf.zeros(dtype=tf.float32, shape=(784, 10)),
+          name='weights',
+          trainable=True),
+      bias=tf.Variable(
+          lambda: tf.zeros(dtype=tf.float32, shape=(10)),
+          name='bias',
+          trainable=True),
+      num_examples=tf.Variable(0.0, name='num_examples', trainable=False),
+      loss_sum=tf.Variable(0.0, name='loss_sum', trainable=False),
+      accuracy_sum=tf.Variable(0.0, name='accuracy_sum', trainable=False))
+
+def mnist_forward_pass(variables, batch):
+  y = tf.nn.softmax(tf.matmul(batch['x'], variables.weights) + variables.bias)
+  predictions = tf.cast(tf.argmax(y, 1), tf.int32)
+
+  flat_labels = tf.reshape(batch['y'], [-1])
+  loss = -tf.reduce_mean(
+      tf.reduce_sum(tf.one_hot(flat_labels, 10) * tf.math.log(y), axis=[1]))
+  accuracy = tf.reduce_mean(
+      tf.cast(tf.equal(predictions, flat_labels), tf.float32))
+
+  num_examples = tf.cast(tf.size(batch['y']), tf.float32)
+
+  variables.num_examples.assign_add(num_examples)
+  variables.loss_sum.assign_add(loss * num_examples)
+  variables.accuracy_sum.assign_add(accuracy * num_examples)
+
+  return loss, predictions
+
+def get_local_mnist_metrics(variables):
+  return collections.OrderedDict(
+      num_examples=variables.num_examples,
+      loss=variables.loss_sum / variables.num_examples,
+      accuracy=variables.accuracy_sum / variables.num_examples)
+
+@tff.federated_computation
+def aggregate_mnist_metrics_across_clients(metrics):
+  return collections.OrderedDict(
+      num_examples=tff.federated_sum(metrics.num_examples),
+      loss=tff.federated_mean(metrics.loss, metrics.num_examples),
+      accuracy=tff.federated_mean(metrics.accuracy, metrics.num_examples))
+
+class MnistModel(tff.learning.Model):
+
+  def __init__(self):
+    self._variables = create_mnist_variables()
+
+  @property
+  def trainable_variables(self):
+    return [self._variables.weights, self._variables.bias]
+
+  @property
+  def non_trainable_variables(self):
+    return []
+
+  @property
+  def local_variables(self):
+    return [
+        self._variables.num_examples, self._variables.loss_sum,
+        self._variables.accuracy_sum
+    ]
+
+  @property
+  def input_spec(self):
+    return collections.OrderedDict(
+        x=tf.TensorSpec([None, 784], tf.float32),
+        y=tf.TensorSpec([None, 1], tf.int32))
+
+    
+
+  @tf.function
+  def forward_pass(self, batch, training=True):
+    del training
+    loss, predictions = mnist_forward_pass(self._variables, batch)
+    num_exmaples = tf.shape(batch['x'])[0]
+    return tff.learning.BatchOutput(
+        loss=loss, predictions=predictions, num_examples=num_exmaples)
+
+  @tf.function
+  def report_local_outputs(self):
+    return get_local_mnist_metrics(self._variables)
+
+  @property
+  def federated_output_computation(self):
+    return aggregate_mnist_metrics_across_clients
\ No newline at end of file

src/participation-hub/federated-learning-microservice/app/processing/preprocessing.py

+import collections
+import attr
+import functools
+import numpy as np
+import tensorflow as tf
+import tensorflow_federated as tff
+
+np.random.seed(0)
+
+emnist_train, emnist_test = tff.simulation.datasets.emnist.load_data()
+
+# NUM_CLIENTS = 10
+# BATCH_SIZE = 20
+
+def preprocess(dataset, BATCH_SIZE = 20):
+
+  def batch_format_fn(element):
+    """Flatten a batch of EMNIST data and return a (features, label) tuple."""
+    return (tf.reshape(element['pixels'], [-1, 784]), 
+            tf.reshape(element['label'], [-1, 1]))
+
+  return dataset.batch(BATCH_SIZE).map(batch_format_fn)
+
+def get_client_ids(emnist_train, NUM_CLIENTS = 10):
+    return np.random.choice(emnist_train.client_ids, size=NUM_CLIENTS, replace=False)
+    
+
+#client_ids = np.random.choice(emnist_train.client_ids, size=NUM_CLIENTS, replace=False)
+
+def get_federated_train_data(emnist_train, NUM_CLIENTS = 10):
+    client_ids = get_client_ids(emnist_train, NUM_CLIENTS)
+    return [preprocess(emnist_train.create_tf_dataset_for_client(x))
+                for x in client_ids
+                ]
+
+
+
+# federated_train_data = [preprocess(emnist_train.create_tf_dataset_for_client(x))
+#   for x in client_ids
+# ]
+
+##################################################################
+## Second Dataset
+
+############################################
+def preprocess_and_shuffle(dataset):
+  """Applies `preprocess_dataset` above and shuffles the result."""
+  preprocessed = preprocess(dataset)
+  return preprocessed.shuffle(buffer_size=5)
\ No newline at end of file

src/participation-hub/federated-learning-microservice/app/prototypeImplementation.py

+import collections
+import attr
+import functools
+import numpy as np
+import tensorflow as tf
+import tensorflow_federated as tff
+
+np.random.seed(0)
+
+#############################################################################
+## LOADING DATASET PART
+emnist_train, emnist_test = tff.simulation.datasets.emnist.load_data()
+useless_param = 1
+NUM_CLIENTS = 10
+BATCH_SIZE = 20
+
+def preprocess(dataset):
+
+  def batch_format_fn(element):
+    """Flatten a batch of EMNIST data and return a (features, label) tuple."""
+    return (tf.reshape(element['pixels'], [-1, 784]), 
+            tf.reshape(element['label'], [-1, 1]))
+
+  return dataset.batch(BATCH_SIZE).map(batch_format_fn)
+
+client_ids = np.random.choice(emnist_train.client_ids, size=NUM_CLIENTS, replace=False)
+
+federated_train_data = [preprocess(emnist_train.create_tf_dataset_for_client(x))
+  for x in client_ids
+]
+
+##################################################################
+## Second Dataset
+
+############################################
+def preprocess_and_shuffle(dataset):
+  """Applies `preprocess_dataset` above and shuffles the result."""
+  preprocessed = preprocess(dataset)
+  return preprocessed.shuffle(buffer_size=5)
+
+preprocessed_and_shuffled = emnist_train.preprocess(preprocess_and_shuffle)
+
+# The type signature will remain the same, but the batches will be shuffled.
+# first_client_dataset = preprocessed_and_shuffled.create_tf_dataset_for_client(
+#     first_client_id)
+# print(first_client_dataset.element_spec)
+first_client_id = emnist_train.client_ids[0]
+first_client_dataset = emnist_train.create_tf_dataset_for_client(
+    first_client_id)
+first_client_dataset = preprocessed_and_shuffled.create_tf_dataset_for_client(
+    first_client_id)
+
+### FINISHED LOADING DATASET PART
+##################################################
+### BASE NON-FEDERATED MODEL PART
+
+def create_keras_model():
+  return tf.keras.models.Sequential([
+      tf.keras.layers.Input(shape=(784,)),
+      tf.keras.layers.Dense(10, kernel_initializer='zeros'),
+      tf.keras.layers.Softmax(),
+  ])
+
+
+def model_fn():
+  keras_model = create_keras_model()
+  return tff.learning.from_keras_model(
+      keras_model,
+      input_spec=federated_train_data[0].element_spec,
+      #input_spec=first_client_dataset.element_spec, # should be the same as above
+      loss=tf.keras.losses.SparseCategoricalCrossentropy(),
+      metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
+
+MnistVariables = collections.namedtuple(
+    'MnistVariables', 'weights bias num_examples loss_sum accuracy_sum')
+
+def create_mnist_variables():
+  return MnistVariables(
+      weights=tf.Variable(
+          lambda: tf.zeros(dtype=tf.float32, shape=(784, 10)),
+          name='weights',
+          trainable=True),
+      bias=tf.Variable(
+          lambda: tf.zeros(dtype=tf.float32, shape=(10)),
+          name='bias',
+          trainable=True),
+      num_examples=tf.Variable(0.0, name='num_examples', trainable=False),
+      loss_sum=tf.Variable(0.0, name='loss_sum', trainable=False),
+      accuracy_sum=tf.Variable(0.0, name='accuracy_sum', trainable=False))
+
+def mnist_forward_pass(variables, batch):
+  y = tf.nn.softmax(tf.matmul(batch['x'], variables.weights) + variables.bias)
+  predictions = tf.cast(tf.argmax(y, 1), tf.int32)
+
+  flat_labels = tf.reshape(batch['y'], [-1])
+  loss = -tf.reduce_mean(
+      tf.reduce_sum(tf.one_hot(flat_labels, 10) * tf.math.log(y), axis=[1]))
+  accuracy = tf.reduce_mean(
+      tf.cast(tf.equal(predictions, flat_labels), tf.float32))
+
+  num_examples = tf.cast(tf.size(batch['y']), tf.float32)
+
+  variables.num_examples.assign_add(num_examples)
+  variables.loss_sum.assign_add(loss * num_examples)
+  variables.accuracy_sum.assign_add(accuracy * num_examples)
+
+  return loss, predictions
+
+def get_local_mnist_metrics(variables):
+  return collections.OrderedDict(
+      num_examples=variables.num_examples,
+      loss=variables.loss_sum / variables.num_examples,
+      accuracy=variables.accuracy_sum / variables.num_examples)
+
+@tff.federated_computation
+def aggregate_mnist_metrics_across_clients(metrics):
+  return collections.OrderedDict(
+      num_examples=tff.federated_sum(metrics.num_examples),
+      loss=tff.federated_mean(metrics.loss, metrics.num_examples),
+      accuracy=tff.federated_mean(metrics.accuracy, metrics.num_examples))
+
+
+
+class MnistModel(tff.learning.Model):
+
+  def __init__(self):
+    self._variables = create_mnist_variables()
+
+  @property
+  def trainable_variables(self):
+    return [self._variables.weights, self._variables.bias]
+
+  @property
+  def non_trainable_variables(self):
+    return []
+
+  @property
+  def local_variables(self):
+    return [
+        self._variables.num_examples, self._variables.loss_sum,
+        self._variables.accuracy_sum
+    ]
+
+  @property
+  def input_spec(self):
+    return collections.OrderedDict(
+        x=tf.TensorSpec([None, 784], tf.float32),
+        y=tf.TensorSpec([None, 1], tf.int32))
+
+  @tf.function
+  def forward_pass(self, batch, training=True):
+    del training
+    loss, predictions = mnist_forward_pass(self._variables, batch)
+    num_exmaples = tf.shape(batch['x'])[0]
+    return tff.learning.BatchOutput(
+        loss=loss, predictions=predictions, num_examples=num_exmaples)
+
+  @tf.function
+  def report_local_outputs(self):
+    return get_local_mnist_metrics(self._variables)
+
+  @property
+  def federated_output_computation(self):
+    return aggregate_mnist_metrics_across_clients
+
+
+### FINISHED MODEL CONSTRUCTION PART
+##################################
+### ????FEDERATED MODEL/TRAINING ALGORITHMS   PART???
+
+# def initialize_fn():
+#   model = model_fn()
+#   return model.trainable_variables
+
+# def next_fn(server_weights, federated_dataset):
+#   # Broadcast the server weights to the clients.
+#   server_weights_at_client = broadcast(server_weights)
+
+#   # Each client computes their updated weights.
+#   client_weights = client_update(federated_dataset, server_weights_at_client)
+
+#   # The server averages these updates.
+#   mean_client_weights = mean(client_weights)
+
+#   # The server updates its model.
+#   server_weights = server_update(mean_client_weights)
+
+#   return server_weights
+
+######################
+### function for next_fn calculation
+
+@tf.function
+def client_update(model, dataset, server_weights, client_optimizer):
+  """Performs training (using the server model weights) on the client's dataset."""
+  # Initialize the client model with the current server weights.
+  client_weights = model.trainable_variables
+  # Assign the server weights to the client model.
+  tf.nest.map_structure(lambda x, y: x.assign(y),
+                        client_weights, server_weights)
+
+  # Use the client_optimizer to update the local model.
+  for batch in dataset:
+    with tf.GradientTape() as tape:
+      # Compute a forward pass on the batch of data
+      outputs = model.forward_pass(batch)
+
+    # Compute the corresponding gradient
+    grads = tape.gradient(outputs.loss, client_weights)
+    grads_and_vars = zip(grads, client_weights)
+
+    # Apply the gradient using a client optimizer.
+    client_optimizer.apply_gradients(grads_and_vars)
+
+  return client_weights
+
+@tf.function
+def server_update(model, mean_client_weights):
+  """Updates the server model weights as the average of the client model weights."""
+  model_weights = model.trainable_variables
+  # Assign the mean client weights to the server model.
+  tf.nest.map_structure(lambda x, y: x.assign(y),
+                        model_weights, mean_client_weights)
+  return model_weights
+
+
+
+#####################################################################################
+##### FEDERATED CORE  / NOT USED?
+####################################################################
+
+# federated_float_on_clients = tff.FederatedType(tf.float32, tff.CLIENTS)
+# print(str(federated_float_on_clients))
+
+@tff.federated_computation(tff.FederatedType(tf.float32, tff.CLIENTS))
+def get_average_temperature(client_temperatures):
+  return tff.federated_mean(client_temperatures)
+
+# print("get_average_temperature.type_signature")
+# print(str(get_average_temperature.type_signature))
+# print(str(get_average_temperature([68.5, 70.3, 69.8])))
+
+
+@tff.tf_computation(tf.float32)
+def add_half(x):
+  return tf.add(x, 0.5)
+
+@tff.federated_computation(tff.FederatedType(tf.float32, tff.CLIENTS))
+def add_half_on_clients(x):
+  return tff.federated_map(add_half, x)
+
+# print("add_half_on_clients.type_signature")
+# print(str(add_half_on_clients.type_signature))
+
+########## ^ NOT USED
+
+##############################################################
+##### BUILDING A TENSOR FLOW FEDERATED MODEL??? FROM SCRATCH????  RECREATING THE FEDERATED MODEL FROM LINE 53
+#https://www.tensorflow.org/federated/tutorials/building_your_own_federated_learning_algorithm#tensorflow_federated_blocks
+
+#Creating the initialization computation
+@tff.tf_computation
+def server_init():
+  model = MnistModel() #model_fn()
+  return model.trainable_variables
+
+
+@tff.federated_computation
+def initialize_fn():
+  return tff.federated_value(server_init(), tff.SERVER)
+
+#Creating the next_fn
+
+#Getting the data type, needed explicitily in the modell functions
+whimsy_model = MnistModel() #model_fn()
+tf_dataset_type = tff.SequenceType(whimsy_model.input_spec)
+print("tf_dataset_type")
+print(str(tf_dataset_type))
+
+model_weights_type = server_init.type_signature.result
+print("model_weights_type")
+print(str(model_weights_type))
+# finished printing types
+
+@tff.tf_computation(tf_dataset_type, model_weights_type)
+def client_update_fn(tf_dataset, server_weights):
+  model = MnistModel()#model_fn()
+  client_optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)
+  return client_update(model, tf_dataset, server_weights, client_optimizer)
+
+@tff.tf_computation(model_weights_type)
+def server_update_fn(mean_client_weights):
+  model = MnistModel()#model_fn()
+  return server_update(model, mean_client_weights)
+
+federated_server_type = tff.FederatedType(model_weights_type, tff.SERVER)
+federated_dataset_type = tff.FederatedType(tf_dataset_type, tff.CLIENTS)
+
+
+@tff.federated_computation(federated_server_type, federated_dataset_type)
+def next_fn(server_weights, federated_dataset):
+  # Broadcast the server weights to the clients.
+  server_weights_at_client = tff.federated_broadcast(server_weights)
+
+  # Each client computes their updated weights.
+  client_weights = tff.federated_map(
+      client_update_fn, (federated_dataset, server_weights_at_client))
+
+  # The server averages these updates.
+  mean_client_weights = tff.federated_mean(client_weights)
+
+  # The server updates its model.
+  server_weights = tff.federated_map(server_update_fn, mean_client_weights)
+
+  return server_weights
+
+federated_algorithm = tff.templates.IterativeProcess(
+    initialize_fn=initialize_fn,
+    next_fn=next_fn
+)
+print("federated_algorithm.initialize.type_signature")
+print(str(federated_algorithm.initialize.type_signature))
+
+print("federated_algorithm.next.type_signature")
+print(str(federated_algorithm.next.type_signature))
+
+
+central_emnist_test = emnist_test.create_tf_dataset_from_all_clients().take(1000)
+central_emnist_test = preprocess(central_emnist_test)
+
+def evaluate(server_state):
+  keras_model = create_keras_model()
+  keras_model.compile(
+      loss=tf.keras.losses.SparseCategoricalCrossentropy(),
+      metrics=[tf.keras.metrics.SparseCategoricalAccuracy()]  
+  )
+  keras_model.set_weights(server_state)
+  keras_model.evaluate(central_emnist_test)
+
+server_state = federated_algorithm.initialize()
+evaluate(server_state)
+
+for round in range(15):
+  server_state = federated_algorithm.next(server_state, federated_train_data)
+
+
+
+
+evaluate(server_state)
\ No newline at end of file

src/participation-hub/federated-learning-microservice/app/prototypeLog

+OrderedDict([('label', TensorSpec(shape=(), dtype=tf.int32, name=None)), ('pixels', TensorSpec(shape=(28, 28), dtype=tf.float32, name=None))])
+OrderedDict([('x', TensorSpec(shape=(None, 784), dtype=tf.float32, name=None)), ('y', TensorSpec(shape=(None, 1), dtype=tf.int64, name=None))])
+OrderedDict([('label', TensorSpec(shape=(), dtype=tf.int32, name=None)), ('pixels', TensorSpec(shape=(28, 28), dtype=tf.float32, name=None))])
+OrderedDict([('x', TensorSpec(shape=(None, 784), dtype=tf.float32, name=None)), ('y', TensorSpec(shape=(None, 1), dtype=tf.int64, name=None))])
+OrderedDict([('x', TensorSpec(shape=(None, 784), dtype=tf.float32, name=None)), ('y', TensorSpec(shape=(None, 1), dtype=tf.int64, name=None))])
+OrderedDict([('label', TensorSpec(shape=(), dtype=tf.int32, name=None)), ('pixels', TensorSpec(shape=(28, 28), dtype=tf.float32, name=None))])
+OrderedDict([('x', TensorSpec(shape=(None, 784), dtype=tf.float32, name=None)), ('y', TensorSpec(shape=(None, 1), dtype=tf.int64, name=None))])
+OrderedDict([('label', TensorSpec(shape=(), dtype=tf.int32, name=None)), ('pixels', TensorSpec(shape=(28, 28), dtype=tf.float32, name=None))])
+OrderedDict([('x', TensorSpec(shape=(None, 784), dtype=tf.float32, name=None)), ('y', TensorSpec(shape=(None, 1), dtype=tf.int64, name=None))])
+OrderedDict([('label', TensorSpec(shape=(), dtype=tf.int32, name=None)), ('pixels', TensorSpec(shape=(28, 28), dtype=tf.float32, name=None))])
+OrderedDict([('x', TensorSpec(shape=(None, 784), dtype=tf.float32, name=None)), ('y', TensorSpec(shape=(None, 1), dtype=tf.int64, name=None))])
+OrderedDict([('label', TensorSpec(shape=(), dtype=tf.int32, name=None)), ('pixels', TensorSpec(shape=(28, 28), dtype=tf.float32, name=None))])
+OrderedDict([('x', TensorSpec(shape=(None, 784), dtype=tf.float32, name=None)), ('y', TensorSpec(shape=(None, 1), dtype=tf.int64, name=None))])
+loss 2.7872610092163086, round time 3.271925449371338
+loss 2.7297325134277344, round time 1.1950867176055908
+loss 2.6944544315338135, round time 1.0879030227661133
+loss 2.7312748432159424, round time 1.111928939819336
+loss 2.688326835632324, round time 1.1382708549499512
+OrderedDict([('label', TensorSpec(shape=(), dtype=tf.int32, name=None)), ('pixels', TensorSpec(shape=(28, 28), dtype=tf.float32, name=None))])
+OrderedDict([('x', TensorSpec(shape=(None, 784), dtype=tf.float32, name=None)), ('y', TensorSpec(shape=(None, 1), dtype=tf.int64, name=None))])
+['f0000_14', 'f0001_41', 'f0005_26', 'f0006_12', 'f0008_45', 'f0011_13', 'f0014_19', 'f0016_39', 'f0017_07', 'f0022_10']
+loss 2.758854627609253, round time 3.2049200534820557
+loss 2.8299858570098877, round time 1.232332706451416
+loss 2.8220741748809814, round time 1.119945764541626
+loss 2.796814203262329, round time 1.0504679679870605
+loss 2.7284183502197266, round time 1.062865972518921
+OrderedDict([('label', TensorSpec(shape=(), dtype=tf.int32, name=None)), ('pixels', TensorSpec(shape=(28, 28), dtype=tf.float32, name=None))])
+OrderedDict([('x', TensorSpec(shape=(None, 784), dtype=tf.float32, name=None)), ('y', TensorSpec(shape=(None, 1), dtype=tf.int64, name=None))])
+['f0000_14', 'f0001_41', 'f0005_26', 'f0006_12', 'f0008_45', 'f0011_13', 'f0014_19', 'f0016_39', 'f0017_07', 'f0022_10']
+loss 2.7316384315490723, round time 3.1910765171051025
+loss 2.7557008266448975, round time 1.3037126064300537
+loss 2.693251371383667, round time 1.0827562808990479
+loss 2.7326154708862305, round time 1.1384437084197998
+loss 2.674974203109741, round time 1.1135189533233643
+dataset computation without preprocessing:
+{float32}@CLIENTS
+get_average_temperature.type_signature
+({float32}@CLIENTS -> float32@SERVER)
+69.53334
+add_half_on_clients.type_signature
+({float32}@CLIENTS -> {float32}@CLIENTS)
+{float32}@CLIENTS
+get_average_temperature.type_signature
+({float32}@CLIENTS -> float32@SERVER)
+69.53334
+add_half_on_clients.type_signature
+({float32}@CLIENTS -> {float32}@CLIENTS)
+tf_dataset_type
+<float32[?,784],int32[?,1]>*
+model_weights_type
+<float32[784,10],float32[10]>
+federated_algorithm.initialize.type_signature
+( -> <float32[784,10],float32[10]>@SERVER)
+federated_algorithm.next.type_signature
+(<server_weights=<float32[784,10],float32[10]>@SERVER,federated_dataset={<float32[?,784],int32[?,1]>*}@CLIENTS> -> <float32[784,10],float32[10]>@SERVER)
+      1/Unknown - 0s 176us/step - loss: 2.3026 - sparse_categorical_accuracy: 0.2000
     38/Unknown - 0s 1ms/step - loss: 2.3026 - sparse_categorical_accuracy: 0.0974  
50/50 [==============================] - 0s 2ms/step - loss: 2.3026 - sparse_categorical_accuracy: 0.0950
+      1/Unknown - 0s 92us/step - loss: 2.1347 - sparse_categorical_accuracy: 0.2500
     43/Unknown - 0s 1ms/step - loss: 2.1636 - sparse_categorical_accuracy: 0.2465 
50/50 [==============================] - 0s 1ms/step - loss: 2.1644 - sparse_categorical_accuracy: 0.2470
+{float32}@CLIENTS
+get_average_temperature.type_signature
+({float32}@CLIENTS -> float32@SERVER)
+69.53334
+add_half_on_clients.type_signature
+({float32}@CLIENTS -> {float32}@CLIENTS)
+tf_dataset_type
+<float32[?,784],int32[?,1]>*
+model_weights_type
+<float32[784,10],float32[10]>
+federated_algorithm.initialize.type_signature
+( -> <float32[784,10],float32[10]>@SERVER)
+federated_algorithm.next.type_signature
+(<server_weights=<float32[784,10],float32[10]>@SERVER,federated_dataset={<float32[?,784],int32[?,1]>*}@CLIENTS> -> <float32[784,10],float32[10]>@SERVER)
+      1/Unknown - 0s 170us/step - loss: 2.3026 - sparse_categorical_accuracy: 0.0000e+00
     41/Unknown - 0s 1ms/step - loss: 2.3026 - sparse_categorical_accuracy: 0.0976      
50/50 [==============================] - 0s 2ms/step - loss: 2.3026 - sparse_categorical_accuracy: 0.0950
+      1/Unknown - 0s 70us/step - loss: 2.2103 - sparse_categorical_accuracy: 0.2000
     41/Unknown - 0s 1ms/step - loss: 2.1651 - sparse_categorical_accuracy: 0.2500 
50/50 [==============================] - 0s 1ms/step - loss: 2.1704 - sparse_categorical_accuracy: 0.2450
+{float32}@CLIENTS
+get_average_temperature.type_signature
+({float32}@CLIENTS -> float32@SERVER)
+69.53334
+add_half_on_clients.type_signature
+({float32}@CLIENTS -> {float32}@CLIENTS)
+tf_dataset_type
+<float32[?,784],int32[?,1]>*
+model_weights_type
+<float32[784,10],float32[10]>
+federated_algorithm.initialize.type_signature
+( -> <float32[784,10],float32[10]>@SERVER)
+federated_algorithm.next.type_signature
+(<server_weights=<float32[784,10],float32[10]>@SERVER,federated_dataset={<float32[?,784],int32[?,1]>*}@CLIENTS> -> <float32[784,10],float32[10]>@SERVER)
+      1/Unknown - 0s 183us/step - loss: 2.3026 - sparse_categorical_accuracy: 0.0500
     45/Unknown - 0s 1ms/step - loss: 2.3026 - sparse_categorical_accuracy: 0.1000  
50/50 [==============================] - 0s 2ms/step - loss: 2.3026 - sparse_categorical_accuracy: 0.0970
+      1/Unknown - 0s 122us/step - loss: 2.2197 - sparse_categorical_accuracy: 0.0500
     48/Unknown - 0s 1ms/step - loss: 2.1573 - sparse_categorical_accuracy: 0.2719  
50/50 [==============================] - 0s 1ms/step - loss: 2.1577 - sparse_categorical_accuracy: 0.2720
+{float32}@CLIENTS
+get_average_temperature.type_signature
+({float32}@CLIENTS -> float32@SERVER)
+69.53334
+add_half_on_clients.type_signature
+({float32}@CLIENTS -> {float32}@CLIENTS)
+{float32}@CLIENTS
+get_average_temperature.type_signature
+({float32}@CLIENTS -> float32@SERVER)
+69.53334
+add_half_on_clients.type_signature
+({float32}@CLIENTS -> {float32}@CLIENTS)
+{float32}@CLIENTS
+get_average_temperature.type_signature
+({float32}@CLIENTS -> float32@SERVER)
+69.53334
+add_half_on_clients.type_signature
+({float32}@CLIENTS -> {float32}@CLIENTS)
+{float32}@CLIENTS
+get_average_temperature.type_signature
+({float32}@CLIENTS -> float32@SERVER)
+69.53334
+add_half_on_clients.type_signature
+({float32}@CLIENTS -> {float32}@CLIENTS)
+tf_dataset_type
+<x=float32[?,784],y=int32[?,1]>*
+model_weights_type
+<float32[784,10],float32[10]>
+federated_algorithm.initialize.type_signature
+( -> <float32[784,10],float32[10]>@SERVER)
+federated_algorithm.next.type_signature
+(<server_weights=<float32[784,10],float32[10]>@SERVER,federated_dataset={<x=float32[?,784],y=int32[?,1]>*}@CLIENTS> -> <float32[784,10],float32[10]>@SERVER)
+      1/Unknown - 0s 172us/step - loss: 2.3026 - sparse_categorical_accuracy: 0.1000
     39/Unknown - 0s 1ms/step - loss: 2.3026 - sparse_categorical_accuracy: 0.0949  
50/50 [==============================] - 0s 2ms/step - loss: 2.3026 - sparse_categorical_accuracy: 0.0970
+      1/Unknown - 0s 78us/step - loss: 2.1794 - sparse_categorical_accuracy: 0.3500
     45/Unknown - 0s 1ms/step - loss: 2.1638 - sparse_categorical_accuracy: 0.2633 
50/50 [==============================] - 0s 1ms/step - loss: 2.1641 - sparse_categorical_accuracy: 0.2610
+tf_dataset_type
+<x=float32[?,784],y=int32[?,1]>*
+model_weights_type
+<float32[784,10],float32[10]>
+federated_algorithm.initialize.type_signature
+( -> <float32[784,10],float32[10]>@SERVER)
+federated_algorithm.next.type_signature
+(<server_weights=<float32[784,10],float32[10]>@SERVER,federated_dataset={<x=float32[?,784],y=int32[?,1]>*}@CLIENTS> -> <float32[784,10],float32[10]>@SERVER)
+      1/Unknown - 0s 167us/step - loss: 2.3026 - sparse_categorical_accuracy: 0.0500
     34/Unknown - 0s 2ms/step - loss: 2.3026 - sparse_categorical_accuracy: 0.0765  
50/50 [==============================] - 0s 2ms/step - loss: 2.3026 - sparse_categorical_accuracy: 0.0880
+      1/Unknown - 0s 93us/step - loss: 2.0796 - sparse_categorical_accuracy: 0.2500
50/50 [==============================] - 0s 1ms/step - loss: 2.1699 - sparse_categorical_accuracy: 0.2430
+tf_dataset_type
+<x=float32[?,784],y=int32[?,1]>*
+model_weights_type
+<float32[784,10],float32[10]>
+federated_algorithm.initialize.type_signature
+( -> <float32[784,10],float32[10]>@SERVER)
+federated_algorithm.next.type_signature
+(<server_weights=<float32[784,10],float32[10]>@SERVER,federated_dataset={<x=float32[?,784],y=int32[?,1]>*}@CLIENTS> -> <float32[784,10],float32[10]>@SERVER)
+      1/Unknown - 0s 160us/step - loss: 2.3026 - sparse_categorical_accuracy: 0.1500
     35/Unknown - 0s 1ms/step - loss: 2.3026 - sparse_categorical_accuracy: 0.1143  
50/50 [==============================] - 0s 2ms/step - loss: 2.3026 - sparse_categorical_accuracy: 0.1050
+      1/Unknown - 0s 71us/step - loss: 2.0558 - sparse_categorical_accuracy: 0.4500
     44/Unknown - 0s 1ms/step - loss: 2.1648 - sparse_categorical_accuracy: 0.2614 
50/50 [==============================] - 0s 1ms/step - loss: 2.1630 - sparse_categorical_accuracy: 0.2630
+tf_dataset_type
+<x=float32[?,784],y=int32[?,1]>*
+model_weights_type
+<float32[784,10],float32[10]>
+federated_algorithm.initialize.type_signature
+( -> <float32[784,10],float32[10]>@SERVER)
+federated_algorithm.next.type_signature
+(<server_weights=<float32[784,10],float32[10]>@SERVER,federated_dataset={<x=float32[?,784],y=int32[?,1]>*}@CLIENTS> -> <float32[784,10],float32[10]>@SERVER)

src/participation-hub/federated-learning-microservice/app/prototypeScript.sh

+#! /bin/bash
+
+#source venv/bin/activate
+
+python prototypeImplementation.py >> prototypeLog

src/participation-hub/federated-learning-microservice/app/services/layer_adapter_service.py

-#global imports
-from db.repository import Repository
-from db.table_repository import TableRepository
-from db.use_case_repository import UseCaseRepository
-from db.entities.layer_adapter import LayerAdapter
-from db.entities.table import Table
-
-from typing import List
-
-class LayerAdapterService:
-
-    _table_repository = TableRepository()
-    _layer_repository = Repository()
-    _use_case_repository = UseCaseRepository()
-    
-    @staticmethod
-    def check_layer(layer: LayerAdapter):
-        '''
-        checks if the given layer has correct mappings regarding the schema of the use_case
-        '''
-        # TODO implement with tables
-        # schema = LayerAdapterService._schema_repository.put(layer.use_case)
-        # for p in layer.properties:
-        #     if p not in schema.mappings:
-        #         raise ValueError(f'{p} is not existent in the schema!')
-
-    @staticmethod
-    def add_complete(layer: LayerAdapter):
-        '''
-        Add a new layer to the DB. Attribute mappings and cluster attributes of the given layer
-        are used. Before inserting, the layer gets checked for consistency with the schema.
-
-        @params:
-            layer - Required : layer object holding correct data
-        '''
-        LayerAdapterService.check_layer(layer)
-        LayerAdapterService._layer_repository.add(layer)
-    
-    @staticmethod
-    def delete_all_use_cases():
-        # TODO
-        LayerAdapterService._layer_repository.delete_all_use_cases()
-        LayerAdapterService._table_repository.delete_all()
-        LayerAdapterService._use_case_repository.delete_all()
-   
\ No newline at end of file

src/participation-hub/federated-learning-microservice/app/slurm-1779.out

+2021-05-04 14:08:53.127296: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2021-05-04 14:09:04.981860: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64
+2021-05-04 14:09:04.982134: W tensorflow/stream_executor/cuda/cuda_driver.cc:312] failed call to cuInit: UNKNOWN ERROR (303)
+2021-05-04 14:09:04.982158: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (mcore2): /proc/driver/nvidia/version does not exist
+2021-05-04 14:09:04.982428: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2021-05-04 14:09:05.002724: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 2599705000 Hz
+2021-05-04 14:09:05.004362: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5583288d82c0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
+2021-05-04 14:09:05.004381: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
+Traceback (most recent call last):
+  File "processing/main_processing.py", line 36, in <module>
+    print(str(server_state.type_signature))
+AttributeError: 'list' object has no attribute 'type_signature'

src/participation-hub/federated-learning-microservice/app/test.py

+import collections
+
+import numpy as np
+import tensorflow as tf
+import tensorflow_federated as tff
+
+#### check if needed   vvv
+from matplotlib import pyplot as plt
+
+################################
+np.random.seed(0)
+
+print(tff.federated_computation(lambda: 'Hello, World!')())
+###########################################################
+####### LOADING DATASET ###################################
+emnist_train, emnist_test = tff.simulation.datasets.emnist.load_data()
+
+example_dataset = emnist_train.create_tf_dataset_for_client(
+    emnist_train.client_ids[0])
+
+###########################################################
+####### Preprocessing data ################################
+NUM_CLIENTS = 5
+NUM_EPOCHS = 5
+BATCH_SIZE = 20
+SHUFFLE_BUFFER = 100
+PREFETCH_BUFFER = 10
+
+def preprocess(dataset):
+
+  def batch_format_fn(element):
+    """Flatten a batch `pixels` and return the features as an `OrderedDict`."""
+    return collections.OrderedDict(
+        x=tf.reshape(element['pixels'], [-1, 784]),
+        y=tf.reshape(element['label'], [-1, 1]))
+
+  return dataset.repeat(NUM_EPOCHS).shuffle(SHUFFLE_BUFFER).batch(
+      BATCH_SIZE).map(batch_format_fn).prefetch(PREFETCH_BUFFER)
+
+
+preprocessed_example_dataset = preprocess(example_dataset)
+
+sample_batch = tf.nest.map_structure(lambda x: x.numpy(),
+                                     next(iter(preprocessed_example_dataset)))
+
+print("sample_batch")
+print(sample_batch)
+
+def make_federated_data(client_data, client_ids):
+  return [
+      preprocess(client_data.create_tf_dataset_for_client(x))
+      for x in client_ids
+  ]
+
+sample_clients = emnist_train.client_ids[0:NUM_CLIENTS]
+
+federated_train_data = make_federated_data(emnist_train, sample_clients)
+
+print('Number of client datasets: {l}'.format(l=len(federated_train_data)))
+print('First dataset: {d}'.format(d=federated_train_data[0]))
+#######################################################################################
+###### Creating the tutorial model
+#######################################################################################
+# def create_keras_model():
+#   return tf.keras.models.Sequential([
+#       tf.keras.layers.InputLayer(input_shape=(784,)),
+#       tf.keras.layers.Dense(10, kernel_initializer='zeros'),
+#       tf.keras.layers.Softmax(),
+#   ])
+
+# def model_fn():
+#   # We _must_ create a new model here, and _not_ capture it from an external
+#   # scope. TFF will call this within different graph contexts.
+#   keras_model = create_keras_model()
+#   return tff.learning.from_keras_model(
+#       keras_model,
+#       input_spec=preprocessed_example_dataset.element_spec,
+#       loss=tf.keras.losses.SparseCategoricalCrossentropy(),
+#       metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
+
+# #######################################################################################
+
+# ####Tutorial implementation
+# iterative_process = tff.learning.build_federated_averaging_process(
+#     model_fn,
+#     client_optimizer_fn=lambda: tf.keras.optimizers.SGD(learning_rate=0.02),
+#     server_optimizer_fn=lambda: tf.keras.optimizers.SGD(learning_rate=1.0))
+# print("\nInitialize Iterative Procees signature:")
+# print(str(iterative_process.initialize.type_signature)) ## Just a print for info
+
+# state = iterative_process.initialize()
+
+
+# NUM_ROUNDS = 10
+# print("\nStarting {} rounds of training:".format(NUM_ROUNDS))
+# for round_num in range(1, NUM_ROUNDS+1):
+#   state, metrics = iterative_process.next(state, federated_train_data)
+#   print('round {:2d}, metrics={}'.format(round_num, metrics))
+
+#######################################################################################
+####### Customizing the model implementation ##########################################
+#######################################################################################
+print(" STAAARTIIING")
+MnistVariables = collections.namedtuple(
+    'MnistVariables', 'weights bias num_examples loss_sum accuracy_sum')
+
+def create_mnist_variables():
+  return MnistVariables(
+      weights=tf.Variable(
+          lambda: tf.zeros(dtype=tf.float32, shape=(784, 10)),
+          name='weights',
+          trainable=True),
+      bias=tf.Variable(
+          lambda: tf.zeros(dtype=tf.float32, shape=(10)),
+          name='bias',
+          trainable=True),
+      num_examples=tf.Variable(0.0, name='num_examples', trainable=False),
+      loss_sum=tf.Variable(0.0, name='loss_sum', trainable=False),
+      accuracy_sum=tf.Variable(0.0, name='accuracy_sum', trainable=False))
+
+def mnist_forward_pass(variables, batch):
+  y = tf.nn.softmax(tf.matmul(batch['x'], variables.weights) + variables.bias)
+  predictions = tf.cast(tf.argmax(y, 1), tf.int32)
+
+  flat_labels = tf.reshape(batch['y'], [-1])
+  loss = -tf.reduce_mean(
+      tf.reduce_sum(tf.one_hot(flat_labels, 10) * tf.math.log(y), axis=[1]))
+  accuracy = tf.reduce_mean(
+      tf.cast(tf.equal(predictions, flat_labels), tf.float32))
+
+  num_examples = tf.cast(tf.size(batch['y']), tf.float32)
+
+  variables.num_examples.assign_add(num_examples)
+  variables.loss_sum.assign_add(loss * num_examples)
+  variables.accuracy_sum.assign_add(accuracy * num_examples)
+
+  return loss, predictions
+
+def get_local_mnist_metrics(variables):
+  return collections.OrderedDict(
+      num_examples=variables.num_examples,
+      loss=variables.loss_sum / variables.num_examples,
+      accuracy=variables.accuracy_sum / variables.num_examples)
+
+@tff.federated_computation
+def aggregate_mnist_metrics_across_clients(metrics):
+  return collections.OrderedDict(
+      num_examples=tff.federated_sum(metrics.num_examples),
+      loss=tff.federated_mean(metrics.loss, metrics.num_examples),
+      accuracy=tff.federated_mean(metrics.accuracy, metrics.num_examples))
+
+
+class MnistModel(tff.learning.Model):
+
+  def __init__(self):
+    self._variables = create_mnist_variables()
+
+  @property
+  def trainable_variables(self):
+    return [self._variables.weights, self._variables.bias]
+
+  @property
+  def non_trainable_variables(self):
+    return []
+
+  @property
+  def local_variables(self):
+    return [
+        self._variables.num_examples, self._variables.loss_sum,
+        self._variables.accuracy_sum
+    ]
+
+  @property
+  def input_spec(self):
+    return collections.OrderedDict(
+        x=tf.TensorSpec([None, 784], tf.float32),
+        y=tf.TensorSpec([None, 1], tf.int32))
+
+  @tf.function
+  def forward_pass(self, batch, training=True):
+    del training
+    loss, predictions = mnist_forward_pass(self._variables, batch)
+    num_exmaples = tf.shape(batch['x'])[0]
+    return tff.learning.BatchOutput(
+        loss=loss, predictions=predictions, num_examples=num_exmaples)
+
+  @tf.function
+  def report_local_outputs(self):
+    return get_local_mnist_metrics(self._variables)
+
+  @property
+  def federated_output_computation(self):
+    return aggregate_mnist_metrics_across_clients
+
+iterative_process = tff.learning.build_federated_averaging_process(
+    MnistModel,
+    client_optimizer_fn=lambda: tf.keras.optimizers.SGD(learning_rate=0.02))
+
+
+state = iterative_process.initialize()
+
+state, metrics = iterative_process.next(state, federated_train_data)
+NUM_ROUNDS = 10
+print("\nStarting {} rounds of training:".format(NUM_ROUNDS))
+for round_num in range(1, NUM_ROUNDS + 1):
+  state, metrics = iterative_process.next(state, federated_train_data)
+  print('round {:2d}, metrics={}'.format(round_num, metrics))
\ No newline at end of file