Federated learning (FL) has attracted increasing attention as a promising
approach to driving a vast number of end devices with artificial intelligence.
However, it is very challenging to guarantee the efficiency of FL considering
the unreliable nature of end devices while the cost of device-server
communication cannot be neglected. In this paper, we propose SAFA, a
semi-asynchronous FL protocol, to address the problems in federated learning
such as low round efficiency and poor convergence rate in extreme conditions
(e.g., clients dropping offline frequently). We introduce novel designs in the
steps of model distribution, client selection and global aggregation to
mitigate the impacts of stragglers, crashes and model staleness in order to
boost efficiency and improve the quality of the global model. We have conducted
extensive experiments with typical machine learning tasks. The results
demonstrate that the proposed protocol is effective in terms of shortening
federated round duration, reducing local resource wastage, and improving the
accuracy of the global model at an acceptable communication cost.

本文提出 SAFA 半异步联邦学习协议，通过在模型分发、客户端选择和全局聚合的步骤中引入新设计来缓解滞后、崩溃和模型陈旧的影响，以提高效率和改善全局模型的质量，从而在通信成本可接受的情况下缩短联邦循环时间、减少本地资源浪费、提高全局模型的准确性。