Restrictive rules for data sharing in many industries have led to the development of \ac{FL}. \ac{FL} is a \ac{ML} technique that allows distributed clients to train models collaboratively without the need to share their respective training data with others. In this article, we first explore the technical basics of FL and its potential applications. Second, we present a conceptual framework for the adoption of \ac{FL}, mapping organizations along the lines of their \ac{AI} capabilities and environment. We then discuss why exemplary organizations in different industries, including industry consortia, established banks, public authorities, and data-intensive SMEs might consider different approaches to \ac{FL}. To conclude, we argue that \ac{FL} presents an institutional shift with ample interdisciplinary research opportunities for the business and information systems engineering community.

翻译：在许多行业中，数据共享的限制性规则催生了联邦学习（\ac{FL}）的发展。\ac{FL}是一种机器学习（\ac{ML}）技术，允许分布式客户端在无需彼此共享训练数据的情况下协作训练模型。在本文中，我们首先探讨了FL的技术基础及其潜在应用。其次，我们提出了一个用于采纳\ac{FL}的概念框架，根据组织的人工智能（\ac{AI}）能力与环境对其进行映射。随后，我们讨论了不同行业中的典型组织（包括行业联盟、成熟银行、公共机构及数据密集型中小企业）为何可能考虑采用差异化的\ac{FL}策略。最后，我们认为\ac{FL}代表了一种制度性转变，为商业与信息系统工程领域提供了丰富的跨学科研究机遇。