论文标题
跨性别的联合学习：挑战和机遇
Cross-Silo Federated Learning: Challenges and Opportunities
论文作者
论文摘要
联合学习（FL）是一项新兴技术，可以从多个客户培训机器学习模型，同时保持数据分布和私密。根据参与的客户和模型培训量表，可以将联合学习分为两种类型：跨设备FL，客户通常是移动设备，并且客户端数量可以达到数百万的规模；客户是组织或公司，并且客户编号通常很小（例如，一百之内）。尽管现有研究主要集中于跨设备FL，但本文旨在提供跨索洛FL的概述。更具体地说，我们首先讨论了交叉Silo FL的应用，并概述了其主要挑战。然后，我们通过关注与跨设备FL的联系和差异，对Cross-Silo FL挑战的现有方法进行系统的概述。最后，我们讨论未来的方向和开放问题，值得社区的研究工作。
Federated learning (FL) is an emerging technology that enables the training of machine learning models from multiple clients while keeping the data distributed and private. Based on the participating clients and the model training scale, federated learning can be classified into two types: cross-device FL where clients are typically mobile devices and the client number can reach up to a scale of millions; cross-silo FL where clients are organizations or companies and the client number is usually small (e.g., within a hundred). While existing studies mainly focus on cross-device FL, this paper aims to provide an overview of the cross-silo FL. More specifically, we first discuss applications of cross-silo FL and outline its major challenges. We then provide a systematic overview of the existing approaches to the challenges in cross-silo FL by focusing on their connections and differences to cross-device FL. Finally, we discuss future directions and open issues that merit research efforts from the community.
