TEA-fed: time-efficient asynchronous federated learning for edge computing

Abstract

Federated learning (FL) has attracted more and more attention recently. The integration of FL and edge computing makes the edge system more efficient and intelligent. FL usually uses the server to actively select certain edge devices to participate in the global model training. However, the selected edge devices may be stragglers, or even crash during training. Meanwhile, the unselected idle edge devices cannot be fully utilized for training. Therefore, besides the widely studied communication efficiency and data heterogeneity issues in FL, we also take the above time efficiency into consideration, and propose a time-efficient asynchronous federated learning protocol, TEA-Fed, to solve these problems. With TEA-Fed, idle edge devices actively apply for training tasks and participate in model training asynchronously once assigned tasks. Considering that there may be a huge number of edge devices in edge computing, we introduce control parameters to limit the number of devices participating in training the identical model at the same time. Meanwhile, we also introduce caching mechanism and weighted averaging with respect to model staleness in the model aggregation step to reduce the adverse effects of model staleness and further improve the accuracy of the global model. Finally, the experimental results show that the protocol can accelerate the convergence of model training, improve the accuracy, and has robustness to heterogeneous data.