Federated Learning (FL) is a machine learning approach that allows multiple clients to collaboratively learn a shared model without sharing raw data. However, current FL systems provide an all-in-one solution, which can hinder the wide adoption of FL in certain domains such as scientific applications. To overcome this limitation, this paper proposes a decoupling approach that enables clients to customize FL applications with specific data subsystems. To evaluate this approach, the authors develop a framework called Data-Decoupling Federated Learning (DDFL) and compare it with state-of-the-art FL systems that tightly couple data management and computation. Extensive experiments on various datasets and data management subsystems show that DDFL achieves comparable or better performance in terms of training time, inference accuracy, and database query time. Moreover, DDFL provides clients with more options to tune their FL applications regarding data-related metrics. The authors also provide a detailed qualitative analysis of DDFL when integrated with mainstream database systems.
翻译:联邦学习(FL)是一种机器学习方法,它使多个客户能够合作学习一个共享模式,而不共享原始数据,然而,目前的FL系统提供了一个全方位解决方案,这可能会妨碍在某些领域,如科学应用等领域广泛采用FL。为克服这一限制,本文件建议采取一种脱钩方法,使客户能够以特定数据子系统定制FL应用程序。为评估这一方法,作者开发了一个称为DDFL(DDFL)的框架,并将其与数据管理和计算紧密结合的最新FL系统进行比较。关于各种数据集和数据管理子系统的大规模实验表明,DDFL在培训时间、推论准确性和数据库查询时间方面达到可比或更好的业绩。此外,DFL为客户提供了更多选项,使其在数据相关计量系统中的FL应用程序能够调节。作者还详细分析了DFL在与主流数据库系统整合时对DFL(DFL)的质量分析。</s>