Abstract Federated Learning (FL) is a collaboratively decentralized privacy-preserving technology to overcome challenges of data silos and data sensibility. Exactly what research is carrying the research momentum forward is a question of interest to research communities as well as industrial engineering. This study reviews FL and explores the main evolution path for issues exist in FL development process to advance the understanding of FL. This study aims to review prevailing application in industrial engineering to guide for the future landing application. This study also identifies six research fronts to address FL literature and help advance our understanding of FL for future optimization. This study contributes to conclude application in industrial engineering and computer science and summarize a review of applications in FL.

中文翻译：

---

联邦学习中的应用回顾

摘要 联邦学习 (FL) 是一种协作分散的隐私保护技术，可克服数据孤岛和数据敏感性的挑战。究竟是什么研究推动了研究势头向前发展是研究界和工业工程感兴趣的问题。本研究回顾了 FL，并探讨了 FL 发展过程中存在的问题的主要演变路径，以促进对 FL 的理解。本研究旨在回顾工业工程中的普遍应用，以指导未来的落地应用。本研究还确定了六个研究前沿，以解决 FL 文献，并帮助我们加深对 FL 的理解以进行未来优化。本研究有助于总结在工业工程和计算机科学中的应用，并总结对 FL 应用的回顾。