  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.
