title: Adaptive top-k selection in machine learning
body: <p>In federated/ distributed learning, the server will send initially a global model to clients, and each client will train the model locally and then select the top k values, and send only these values to the server.</p>
  <p><strong>How I can select an adaptive k in each client?</strong> rather than set top k value to fixed number (e.g. k=3, which return top 3 values), I want to make the top k values adaptive, for example, some clients will send top 4 values, other may send 6 top values based on a defined feature ( largest value, largest loss , ... etc)</p>
  <p>Is there any way to do that?</p>