Machine Learning (ML) has shown significant potential in various applications; however, its adoption in privacy-critical domains has been limited due to concerns about data privacy. A promising solution to this issue is Federated Machine Learning (FedML), a model-to-data approach that prioritizes data privacy. By enabling ML algorithms to be applied directly to distributed data sources without sharing raw data, FedML offers enhanced privacy protections, making it suitable for privacy-critical environments. Despite its theoretical benefits, FedML has not seen widespread practical implementation. This study aims to explore the current state of applied FedML and identify the challenges hindering its practical adoption. Through a comprehensive systematic literature review, we assess 74 relevant papers to analyze the real-world applicability of FedML. Our analysis focuses on the characteristics and emerging trends of FedML implementations, as well as the motivational drivers and application domains. We also discuss the encountered challenges in integrating FedML into real-life settings. By shedding light on the existing landscape and potential obstacles, this research contributes to the further development and implementation of FedML in privacy-critical scenarios.

翻译：机器学习在各种应用中展现了显著潜力；然而，由于对数据隐私的担忧，其在隐私关键领域的采用一直受到限制。解决这一问题的有前景的途径是联邦机器学习，这是一种优先考虑数据隐私的“模型到数据”方法。通过使机器学习算法能够直接应用于分布式数据源而无需共享原始数据，联邦机器学习提供了增强的隐私保护，使其适合隐私关键环境。尽管具有理论上的优势，联邦机器学习尚未得到广泛的实践应用。本研究旨在探讨联邦机器学习的应用现状，并识别阻碍其实际采用的挑战。通过全面的系统文献综述，我们评估了74篇相关论文，以分析联邦机器学习的真实世界适用性。我们的分析聚焦于联邦机器学习实施的特征与新兴趋势，以及驱动因素和应用领域。我们还讨论了将联邦机器学习融入现实场景时所遇到的挑战。通过揭示现有格局与潜在障碍，本研究为联邦机器学习在隐私关键场景中的进一步发展与实施做出了贡献。