  To date, the most popular federated learning algorithms use coordinate-wise
averaging of the model parameters. We depart from this approach by
differentiating client contributions according to the performance of local
learning and its evolution. The technique is inspired from control theory and
its classification performance is evaluated extensively in IID framework and
compared with FedAvg.
