Federated learning is gaining traction as a method for training deep learning models while respecting user privacy by keeping data on devices. However, the balance between communication efficiency and model performance poses unique challenges, particularly when employing large-batch training methods. The proposed large-batch training technique demonstrates improvements over traditional small-batch methods, achieving higher test accuracy on common models such as ResNet50 and VGG11.
The implications of this research are significant for the development of more efficient federated learning frameworks. By optimizing the training process, deployed applications can potentially witness enhanced model performance without an exponential increase in communication overhead. However, careful consideration must be given to the risk of generalization loss when using large batches. This study encourages further exploration of hybrid approaches that maximize both performance and efficiency in federated learning environments.
👉 Pročitaj original: arXiv AI Papers
