论文标题
实用的一杆联合学习，用于跨索洛设置
Practical One-Shot Federated Learning for Cross-Silo Setting
论文作者
论文摘要
联合学习使多方可以在不交换数据的情况下协作学习模型。尽管大多数现有的联合学习算法都需要许多回合来融合，但单一联合学习（即，使用单个交流回合的联合学习）是一种有希望的方法，可以使联合学习适用于实践中的交叉硅环境。但是，现有的一击算法仅支持特定模型，并且不提供任何隐私保证，从而大大限制了实践中的应用程序。在本文中，我们提出了一种名为Fedkt的联合学习算法的实用的单发算法。通过利用知识转移技术，可以将FEDKT应用于任何分类模型，并可以灵活地获得差异隐私保证。我们在各种任务上的实验表明，FEDKT可以通过单个通信回合大大优于其他最先进的联合学习算法。
Federated learning enables multiple parties to collaboratively learn a model without exchanging their data. While most existing federated learning algorithms need many rounds to converge, one-shot federated learning (i.e., federated learning with a single communication round) is a promising approach to make federated learning applicable in cross-silo setting in practice. However, existing one-shot algorithms only support specific models and do not provide any privacy guarantees, which significantly limit the applications in practice. In this paper, we propose a practical one-shot federated learning algorithm named FedKT. By utilizing the knowledge transfer technique, FedKT can be applied to any classification models and can flexibly achieve differential privacy guarantees. Our experiments on various tasks show that FedKT can significantly outperform the other state-of-the-art federated learning algorithms with a single communication round.
