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 $\textit{may appear only once}$ during training and thus must download the model parameters. In this paper, we propose a new framework ($\texttt{DoCoFL}$) for downlink compression in the cross-device federated learning setting. Importantly, $\texttt{DoCoFL}$ can be seamlessly combined with many uplink compression schemes, rendering it suitable for bi-directional compression. Through extensive evaluation, we demonstrate that $\texttt{DoCoFL}$ offers significant bi-directional bandwidth reduction while achieving competitive accuracy to that of $\texttt{FedAvg}$ without compression.

本文针对跨设备联邦学习的下行（从参数服务器到客户端）通信压缩问题，提出了一种名为DoCoFL的新框架，结合了多种上行压缩方案，可实现双向压缩，显著减少带宽需求，并且准确率达到无压缩时的水平，与FedAvg相当。

跨设备联邦学习下行压缩