Title: QuAFL: Federated Averaging Made Asynchronous and Communication-Efficient. Abstract: Federated Learning (FL) is an emerging paradigm to enable the large-scale distributed training of machine learning models, while still providing privacy guarantees. 
  In this work, we address two of the main practical challenges when scaling federated optimization to large node counts: the need for tight synchronization between the central authority and individual computing nodes, and the large communication cost of transmissions between the central server and clients. 
  Specifically, we present a new variant of the classic federated averaging (FedAvg) algorithm, which supports both asynchronous communication and communication compression. We provide a new analysis technique showing that, in spite of these system relaxations, our algorithm can provide similar convergence to FedAvg in some parameter regimes.
  On the experimental side, we show that our algorithm ensures fast  convergence for standard federated tasks. 