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.
翻译:联邦学习是不同组织在隐私限制下合作培训机器学习模式的一个热门研究课题。研究人员试图支持更多的机器学习模式,采用不同的隐私保护方法,因此需要开发系统和基础设施,以方便各种联合会式学习算法的发展。类似于促进深层学习发展的深层学习系统,联邦学习系统(FLS)同样重要,并面临效力、效率和隐私等各方面的挑战。我们在这次调查中全面审查了联邦化学习系统。为了实现顺利流动并指导未来的研究,我们引入了联邦化学习系统的定义并分析了系统组成部分。此外,我们按照六个不同方面对联邦化学习系统进行了彻底分类,包括数据分配、机器学习模式、隐私机制、通信结构、联邦制规模和联邦动力。分类有助于设计我们案例研究中显示的联邦化学习系统。我们系统地总结了现有的联邦化学习系统,我们介绍了设计因素、案例研究和未来研究机会。