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.

翻译：联邦学习（FL）是机器学习中一个注重隐私保护的子领域，它通过将模型下发至用户设备进行训练，避免了与中心服务器共享个人数据的需要。现有研究虽已解决数据异构性问题，却忽视了联邦学习中的其他挑战，如设备异构性与通信效率。本文提出RE-FL，一种应对资源受限设备中计算与通信挑战的创新方法。我们的变量剪枝技术通过使剪枝策略适应各客户端的计算能力，优化了资源利用率。同时采用知识蒸馏技术降低带宽消耗和通信轮次。在图像分类任务上的实验结果表明，该方法在资源受限环境下保持了数据隐私性与性能，同时兼容异构模型架构。