Many compression techniques have been proposed to reduce the communication overhead of Federated Learning training procedures. However, these are typically designed for compressing model updates, which are expected to decay throughout training. As a result, such methods are inapplicable to downlink (i.e., from the parameter server to clients) compression in the cross-device setting, where heterogeneous clients may appear only once during the training procedure and thus must download the model parameters. In this paper, we propose a new framework (DoCoFL) for downlink compression in the cross-device federated learning setting. Importantly, DoCoFL can be seamlessly combined with many uplink compression schemes, rendering it suitable for bi-directional compression. Through extensive evaluation, we demonstrate that DoCoFL offers significant bi-directional bandwidth reduction while achieving competitive accuracy to that of FedAvg without compression.