In manufacturing settings, data collection and analysis are often a time-consuming, challenging, and costly process. It also hinders the use of advanced machine learning and data-driven methods which require a substantial amount of offline training data to generate good results. It is particularly challenging for small manufacturers who do not share the resources of a large enterprise. Recently, with the introduction of the Internet of Things (IoT), data can be collected in an integrated manner across the factory in real-time, sent to the cloud for advanced analysis, and used to update the machine learning model sequentially. Nevertheless, small manufacturers face two obstacles in reaping the benefits of IoT: they may be unable to afford or generate enough data to operate a private cloud, and they may be hesitant to share their raw data with a public cloud. Federated learning (FL) is an emerging concept of collaborative learning that can help small-scale industries address these issues and learn from each other without sacrificing their privacy. It can bring together diverse and geographically dispersed manufacturers under the same analytics umbrella to create a win-win situation. However, the widespread adoption of FL across multiple manufacturing organizations remains a significant challenge. This study aims to review the challenges and future directions of applying federated learning in the manufacturing industry, with a specific emphasis on the perspectives of Industry 4.0 and 5.0.

翻译：在制造场景中，数据采集与分析通常是一个耗时、困难且成本高昂的过程。这也阻碍了需要大量离线训练数据才能产生良好结果的先进机器学习与数据驱动方法的运用。对于不具备大型企业资源的小型制造商而言，这一问题尤为突出。近年来，随着物联网（IoT）的引入，工厂数据可实时集成采集、发送至云端进行高级分析，并用于逐步更新机器学习模型。然而，小型制造商在享受物联网红利时面临两大障碍：他们可能无法负担或生成足够的数据来运营私有云，也可能对将原始数据共享给公共云持谨慎态度。联邦学习（FL）作为一种新兴的协作学习概念，能够帮助小型制造企业解决这些问题，在不牺牲隐私的前提下实现相互学习。它可将分散于不同地域的各类制造商汇聚在同一分析框架下，创造共赢局面。然而，联邦学习在多个制造组织间的广泛部署仍面临重大挑战。本研究旨在综述联邦学习在制造业应用中的挑战与未来发展方向，尤其侧重于工业4.0与5.0视角下的探讨。