Several open-source systems, such as Flower and NVIDIA FLARE, have been developed in recent years while focusing on different aspects of federated learning (FL). Flower is dedicated to implementing a cohesive approach to FL, analytics, and evaluation. Over time, Flower has cultivated extensive strategies and algorithms tailored for FL application development, fostering a vibrant FL community in research and industry. Conversely, FLARE has prioritized the creation of an enterprise-ready, resilient runtime environment explicitly designed for FL applications in production environments. In this paper, we describe our initial integration of both frameworks and show how they can work together to supercharge the FL ecosystem as a whole. Through the seamless integration of Flower and FLARE, applications crafted within the Flower framework can effortlessly operate within the FLARE runtime environment without necessitating any modifications. This initial integration streamlines the process, eliminating complexities and ensuring smooth interoperability between the two platforms, thus enhancing the overall efficiency and accessibility of FL applications.

翻译：近年来，多个开源系统（如Flower与NVIDIA FLARE）相继问世，其研发重点各有侧重。Flower致力于实现联邦学习（FL）、分析与评估的协同方法。随着发展，Flower已积累了大量专为联邦学习应用开发定制的策略与算法，在学术界与工业界培育了活跃的联邦学习社区。与之相对，FLARE则着力构建一个企业级、高可用的运行时环境，专门针对生产环境中的联邦学习应用而设计。本文阐述了我们对这两个框架的初步集成方案，并展示其如何协同增强整体联邦学习生态。通过Flower与FLARE的无缝集成，基于Flower框架开发的应用无需任何修改即可在FLARE运行时环境中顺畅运行。此项初步集成简化了操作流程，消除了系统间的复杂性，确保双平台间的流畅互操作性，从而全面提升联邦学习应用的效率与可及性。