Combating Client Dropouts in Federated Learning by Mimicking Central Updates

The core message of this paper is to propose a novel federated learning algorithm named MimiC that can effectively combat the negative impacts of arbitrary client dropouts by modifying the received model updates to mimic an imaginary central update.