message: Only use aggregator if there is more than one theta update
diff: @@ -61,7 +61,10 @@ class Coordinator:
  # Collect training results from the participants of this round
  theta_updates, histories = self.train_local_concurrently(theta, participants)
  # Aggregate training results
  + if len(theta_updates) > 1:
  theta_prime = self.aggregator.aggregate(theta_updates)
  + elif len(theta_updates) == 1:
  + theta_prime = theta_updates[0]
  # Update own model parameters
  self.model.set_weights(theta_prime)
  return histories
  