联邦学习系统综述：数据隐私和保护的愿景、炒作和现实
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
摘要：
联邦学习是在隐私限制下实现不同组织间机器学习模型协同训练的一个研究热点。随着研究人员试图用不同的隐私保护方法支持更多的机器学习模型，开发系统和基础设施时需要简化各种联邦学习算法的开发。与PyTorch和TensorFlow等深度学习系统类似，联邦学习系统（FLS）同样重要，并面临来自有效性，效率和隐私等各个方面的挑战。在本调查中，我们对联邦学习系统进行了全面的综述。为了实现流畅的流程和指导未来的研究，我们介绍了联邦学习系统的定义和分析的系统组件。从数据分布、机器学习模型、隐私机制、通信结构、联邦规模和联邦动机六个方面对联邦学习系统进行了分类。正如我们的案例研究所示，分类可以帮助设计联邦学习系统。通过对现有的联邦学习系统进行系统总结，提出了联邦学习系统的设计要素、案例分析和未来的研究方向。
英文摘要：
Federated learning has been a hot research topic in enabling the collaborative training of machine learning models among different organizations under the privacy restrictions. As researchers try to support more machine learning models with different privacy-preserving approaches, there is a requirement in developing systems and infrastructures to ease the development of various federated learning algorithms. Similar to deep learning systems such as PyTorch and TensorFlow that boost the development of deep learning, federated learning systems (FLSs) are equivalently important, and face challenges from various aspects such as effectiveness, efficiency, and privacy. In this survey, we conduct a comprehensive review on federated learning systems. To achieve smooth flow and guide future research, we introduce the definition of federated learning systems and analyze the system components. Moreover, we provide a thorough categorization for federated learning systems according to six different aspects, including data distribution, machine learning model, privacy mechanism, communication architecture, scale of federation and motivation of federation. The categorization can help the design of federated learning systems as shown in our case studies. By systematically summarizing the existing federated learning systems, we present the design factors, case studies, and future research opportunities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
