Communication on heterogeneous edge networks is a fundamental bottleneck in
Federated Learning (FL), restricting both model capacity and user
participation. To address this issue, we introduce two novel strategies to
reduce communication costs: (1) the use of lossy compression on the global
model sent server-to-client; and (2) Federated Dropout, which allows users to
efficiently train locally on smaller subsets of the global model and also
provides a reduction in both client-to-server communication and local
computation. We empirically show that these strategies, combined with existing
compression approaches for client-to-server communication, collectively provide
up to a $14\times$ reduction in server-to-client communication, a $1.7\times$
reduction in local computation, and a $28\times$ reduction in upload
communication, all without degrading the quality of the final model. We thus
comprehensively reduce FL's impact on client device resources, allowing higher
capacity models to be trained, and a more diverse set of users to be reached.

采用两种新策略（global model 压缩和 Federated Dropout）和现有压缩方法的结合，可以综合降低联邦学习对服务器到客户端通信的成本高达 14 倍、本地计算量 1.7 倍并在上传通信方面降低 28 倍，同时不降低最终模型的质量，从而全面降低了联邦学习对客户端设备资源的影响。