DoCoFL: Downlink Compression for Cross-Device Federated Learning

Understand

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.
• Accordingly, we propose $\textsf{DoCoFL}$ -- a new framework for downlink compression in the cross-device setting.