How implement differential privacy in federated learning

Question

I'm beginner in federated learning. I try to add gaussian noise to gradient in client_updata. If anyone attempt to do , please teach me how to do. Thank you in advance.

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.
    # Update weights
    client_optimizer.apply_gradients(grads_and_vars)
  
  return client_weights

Answer 1

See tutorial: Differential Privacy in TFF