标题： FedERL：联邦高效鲁棒学习用于常见损坏 显示英文标题

标题： FedERL: Federated Efficient and Robust Learning for Common Corruptions

摘要： 联邦学习（FL）在保护数据隐私的同时加速了深度学习模型在边缘设备上的部署。 然而，由于客户端计算资源的限制，以及对常见损坏（如噪声、模糊和天气影响）缺乏鲁棒性，FL系统面临挑战。 现有的鲁棒训练方法计算成本高，不适合资源受限的客户端。 我们提出了FedERL，即联邦高效鲁棒学习，作为首次在客户端时间和能量约束下明确解决损坏鲁棒性的作品。 其核心是，FedERL在服务器端采用一种新颖的数据无关鲁棒训练（DART）方法，以在不访问训练数据的情况下增强鲁棒性。 这样，FedERL确保客户端没有鲁棒性开销。 大量实验表明 FedERL能够在传统鲁棒训练方法所需时间和能量成本的一小部分内处理常见损坏。 在时间和能量预算有限的场景中，FedERL的性能优于传统鲁棒训练，使其成为现实世界 FL应用的实际且可扩展的解决方案。 显示英文摘要

摘要： Federated learning (FL) accelerates the deployment of deep learning models on edge devices while preserving data privacy. However, FL systems face challenges due to client-side constraints on computational resources, and from a lack of robustness to common corruptions such as noise, blur, and weather effects. Existing robust training methods are computationally expensive and unsuitable for resource-constrained clients. We propose FedERL, federated efficient and robust learning, as the first work to explicitly address corruption robustness under time and energy constraints on the client side. At its core, FedERL employs a novel data-agnostic robust training (DART) method on the server to enhance robustness without access to the training data. In doing so, FedERL ensures zero robustness overhead for clients. Extensive experiments demonstrate FedERL's ability to handle common corruptions at a fraction of the time and energy cost of traditional robust training methods. In scenarios with limited time and energy budgets, FedERL surpasses the performance of traditional robust training, establishing it as a practical and scalable solution for real-world FL applications.