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.

本文概述了联邦学习领域中的一个新兴分支 “cross-silo FL”，分析了其应用和所面临的主要挑战，并对现有方法进行了系统总结，同时指出了未来需要重点研究的方向和问题。