title: Accuracy is Decreasing Too Slowly with each Epoch in Tensorflow Federated Training
body: <p>My Tensorflow Federated model is taking too long to converge. When I use the same model without TFF wrapping, training it with tensoflow 2.0, the accuracy reaches 0.97 within few epochs. However, with TFF training the same model is able to reach only 0.03 in 30 epochs. What could be the reason for such low accuracy during TFF training. Is there a way to improve this. My code is given below:</p>
  <pre class="lang-py prettyprint-override"><code># Building the Federated Averaging Process
  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))
  
  str(iterative_process.initialize.type_signature)
  
  state = iterative_process.initialize()
  
  state, metrics = iterative_process.next(state, federated_train_data)
  print('round  1, metrics={}'.format(metrics))
  
  NUM_ROUNDS = 1000
  for round_num in range(2, NUM_ROUNDS):
    state, metrics = iterative_process.next(state, federated_train_data)
    print('round {:2d}, metrics={}'.format(round_num, metrics))
  </code></pre>