Federated Learning (FL) is a privacy-enforcing sub-domain of machine learning
that brings the model to the user's device for training, avoiding the need to
share personal data with a central server. While existing works address data
heterogeneity, they overlook other challenges in FL, such as device
heterogeneity and communication efficiency. In this paper, we propose RE-FL, a
novel approach that tackles computational and communication challenges in
resource-constrained devices. Our variable pruning technique optimizes resource
utilization by adapting pruning to each client's computational capabilities. We
also employ knowledge distillation to reduce bandwidth consumption and
communication rounds. Experimental results on image classification tasks
demonstrate the effectiveness of our approach in resource-constrained
environments, maintaining data privacy and performance while accommodating
heterogeneous model architectures.

在本文中，我们提出了一种名为 RE-FL 的新方法，该方法解决了资源受限设备中计算和通信挑战的问题。我们的可变修剪技术通过根据每个客户端的计算能力进行修剪来优化资源利用。我们还使用知识蒸馏来减少带宽消耗和通信轮次。对图像分类任务的实验结果证明了我们的方法在资源受限环境中保持数据隐私和性能的有效性，同时适应了异构模型架构。