Elastic Optimized Edge Federated Learning

To fully exploit the enormous data generated by the devices in edge computing, edge federated learning (EFL) is envisioned as a promising solution. The distributed collaborative training in EFL deals with the delay and privacy issues as compared to the traditional model training. However, the existence of straggling devices degrades the model performance. The stragglers are manifested due to the data or system heterogeneity. In this paper, we introduce elastic optimized edge federated learning (FedEN) approach to mitigate the straggling-effect due to the data heterogeneity. This issue can be alleviated by the reinforced device selection by the edge server which can solve device heterogeneity to some extent. But, the statistical heterogeneity remains unsolved. Specifically, we define the problem of stragglers in EFL. Then, we formulate the optimization problem to be solved at the edge devices. We experimented on the MNIST and CIFAR-10 datasets for the proposed model. Simulated experiments demonstrates that the proposed approach improves the training performance. The results confirm the improved performance of FedEN approach over the baselines.