SAFA: a semi-asynchronous protocol for fast federated learning with low overhead
Research output: Contribution to journal › Article › peer-review
Colleges, School and Institutes
- University of Warwick
- South China University of Technology
- Shenzhen University, Shenzhen, China.
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 article, 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.
|Number of pages||14|
|Journal||IEEE Transactions on Computers|
|Early online date||14 May 2020|
|Publication status||Published - 1 May 2021|