SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead
This addresses communication and reliability bottlenecks in federated learning for edge devices, though it appears incremental as it builds on existing FL methods.
The paper tackles the inefficiency and poor convergence of federated learning under unreliable conditions like client dropouts by proposing SAFA, a semi-asynchronous protocol, which reduces round duration and improves model accuracy with acceptable communication costs.
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.